# Technical Support Document for Technical Products Prepared by the Western Regional Air Partnership (WRAP) in Support of Western Regional Haze Plans

February 28 , 201 1

Prepared and Reviewed by:

Michael Feldman, EPA Region 6 Erik Snyder, E PA Region 6 Kevin Golden, EPA Region 8

Gail Tonnesen , EPA Region 8 Aaron Worstell, EPA Region 8

Larry Biland, EPA Region 9 Carol Bohnenkamp, EPA Region 9 Scott Bohning, EPA Region 9 Gregory Nudd, EPA Region 9 Robert Kotchenruther, EPA Region 10 Keith Rose, EPA Region 10
Tom Moore, Western Governors’ Association/ WRAP2 

### WRAP Regional Haze Technical Support Document (TSD)

Table of Contents

1. BACKGROUND AND INTRO DUCTION TO WRAP TECHNICAL WO RK

................... 3

2. DEVELOPMENT OF VISIB ILITY BASELINES AND PLANNING GOALS

................... 5

3. EMISSIONS INVENTORIE S

................................ ................................ .............................. 12

4. METEOROLOGICAL MODEL ING

................................ ................................ ................... 24

5. VISIBILITY MODELING

................................ ................................ ................................ ... 29

6. SOURCE APPORTIONMENT MODELING

................................ ................................ ...... 43

7. BART MODELING

................................ ................................ ................................ .............. 52

8. CONCLUSIONS

- WRAP TECHNICAL WORK IN SUPPORT OF REGIONAL HAZE

PLANNING

................................ ................................ ................................ ................................ .. 57

A. APPENDIX: ACCESSIN G WRAP TECHNICAL PRO DUCTS

................................ ........ 593 

### 1. Background and Introduction to WRAP Technical Work

A. Background

Regional haze is visibility impairment at federal Class I areas, 118 of which are located in the Western U.S., about 75% of the n ational total of Class I areas. Regional haze is caused overwhelmingly by fine particulates (PM 2.5 ) , with limited impacts from the PM

coarse fraction of

PM 10 at more dusty Class I areas. The greatest fraction of v isibility impairment occurs when

PM 2.5 in the atmosphere scatters and absorbs light, thereby creating haze. PM 2.5 can be emitted into the atmosphere directly as primary particulates, or it can be produced in the atmosphere from photochemical reactions of gas

- phase precursors and subsequent condens ation to form secondary particulates. Examples of primary PM 2.5 include crustal materials and elemental carbon; examples of secondary PM 2.5 include ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). Diameters of s econdary PM 2.5 aero sols are generally smaller than primary PM 2.5 , and because visible light is most affect ed by particles between 0.5 and 1 micrometers, PM 2.5 is very effective at scattering light and impairing visual air quality and the ability to perceive scenic vistas ov er the long distances found in Class I areas of the West. Light scattering is also greater for PM 2.5 than for PM
coarse particles . S econdary PM 2.5 plays an especially important role in visibility impairment. Moreover, the smaller secondary PM 2.5 can remai n suspended in the atmosphere for longer periods and is transported long distances, thereby contributing to regional

- scale impacts of pollutant emissions on visibility.

The sources of PM 2.5 are difficult to quantify because of the complex nature of their formation, transport, and removal from the atmosphere , as well as the interaction between primary and secondary emissions with aero sols, solar radiation, water vapor, and other meteorological

variables in the atmosphere . This makes it difficult to simply u se emissions data to determine which pollutants should be controlled to most effectively improve visibility. Photochemical air quality models offer opportunity to better understand the sources of PM 2.5 by simulating the emissions of pollutants and the form ation, transport, and deposition of PM 2.5 . If an air quality model performs well for a n historical episode, the model may then be useful for identifying the

sources of PM 2.5 and helping to select the most effective emissions reduction strategies for

attain ing visibility goals. Although several types of air quality modeling systems are available, the gridded, three

- dimensional, Eulerian models provide the most complete spatial representation and the most comprehensive representation of processes affecting PM 2.5 , especially for situations in which multiple pollutant sources interact to form PM 2.5

.

In Section 169A of the 1977 Amendments to CAA 1

1 , Congress set forth a program for protecting visibility in the nation’s national parks and wilderness areas. This se ction of the CAA establishes as a national goal the “

... prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Federal Class I areas which impairment results from manmade air pollution.” EPA promulgated a rule to a ddress regional haze on July 1, 1999 (64 FR 35713), the Regional Haze Rule (RHR). The RHR established the goal of achieving “natural” visibility conditions in all 156 f ederal Class I areas by 2064.

http://www.epa.g ov/air/caa/4 Because the pollutants that lead to regional haze can or iginate from sources located across broad geographic areas, EPA has encouraged the States and Tribes across the United States to address visibility impairment from a regional perspective. With the WRAP

, formed in 1997 to implement the Grand Canyon Visibil ity Transport Commission’s recommendations to EPA 2

, four additional regional planning organizations (RPOs) 3 were developed to address regional haze and related issues. One of the main objectives of the RPOs is to analyze available data and conduct polluta nt transport modeling to assist the States in developing their regional haze plans.

The W RAP RPO is a collaborative effort of State governments, tribal governments, and various federal agencies established to conduct data analyses, conduct pollutant tra nsport modeling, and coordinate planning activities among the western States

, addressing regional haze in the numerous and highly diverse Class I areas across the West, ranging from high and low, hot and

cold

- deserts, mountains, seashores, forests and pl ains . WRAP member State and Tribal governments include: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. Tribal board members include Campo Band of Kumeyaay India ns, Confederated Salish and Kootenai Tribes, Cortina Indian Rancheria, Hopi Tribe, Hualapai Nation of the Grand Canyon, Native Village of Shungnak, Nez Perce Tribe, Northern Cheyenne Tribe, Pueblo of Acoma, Pueblo of San Felipe,

and Shoshone

- Bannock Tribes of Fort Hall. WRAP activities were completely open and

inclusive as stipulated in the WRAP Charter 4

. Several additional tribes from across the WRAP region, the State of Nevada, numerous Western local air agencies, environmental groups, industry, academi a, consulting firms, and the general public were closely involved, and provided major technical input contributions to the WRAP analysis process.

B. Technical Requirements for Regional Haze SIPs

The RHR does not mandate specific milestones or rates of pro gress, but instead calls for States to establish goals for 10

- year milestone increments that provide for “reasonable progress” toward achieving natural visibility conditions over a nominal 60

- year period, to steadily and smoothly

reduce emission to achieve natural visibility conditions . In setting Reasonable Progress Goals (RPGs) for each 10

- year target , States must provide for an improvement in visibility for the most impaired days over the 10

- year period of the SIP, and ensure no degradation in visibilit y for the least impaired days over the same period. In setting the RPGs for each 10

- year period covered by a SIP, States must also compare the RPGs to the uniform rate of progress needed to reach natural visibility conditions by 2064, referred to as the “ glide path”, which is the linear rate of reduction in visibility impairment (in deciviews) needed to achieve natural conditions by 2064. The end point of the glide path, EPA default natural conditions values

, must be reviewed and selected or

alternative v alues adopted in each 10

- year SIP cycle, thus the slope of the glide path, the natural conditions target, and the necessary r at e of emissions reduction progress can and will likely change over the nominal 60

- year perio d specified in the RHR, as natural con ditions are better

understood and defin e d.

2 http://www.wrapair.org/WRAP/reports/GCVTCFinal.PDF

3 http://www.epa.gov/air/visibility/regional.ht ml#thefive#thefive

4 http://www.wrapair.org/WRAP/charter/charter.html5 According to the RHR, these first foundational Regional Haze SIPs must specifically identify and address the following elements to specify emissions control s and reasonable progress expected to be achieved betw een the 2000

- 04 baseline and 2018, for each federal Class I area :

i. Baseline Visibility Conditions for the period 2000

- 04

ii. Initial Natural Visibility Conditions estimates for 2064

iii. Uniform Rate of Progress between the se baseline and natural conditions

iv. Best Av ailable Retrofit Technology (BART) to be implemented before 2018

v. Current (2002 or 2000

- 04) and Future (2018) Emission Inventories

vi. Source s’ and Regions’ Contribution s to Haze at each Class I area

vii. Reasonable Progress Goals to be achieved by 2018

The purpos e of this TSD is to review the technical products developed by the WRAP for the

States in the western region, in support of their RH SIPs. This TSD evaluated the methods and procedures used by WRAP to develop products that assisted the western regional Sta tes in addressing the required elements of a RH SIP. Specifically, this TSD reviewed the meteorology, visibility impairment, source apportionment, and BART modeling conducted by WRAP, and determined if these models met applicable guidelines or protocols, a nd met modeling standards at the time they were conducted.

C. Format and Structure of This Document

Many portions of this text come directly from existing WRAP documentation of their technical work. To delineate explanations of WRAP’s work from EPA rev iew of that work, this document is structured such that EPA comments appear in bold face as either ‘Review Comments’ or ‘Explanatory Comments’.



### 2. Development of V isibilit y Baselines and Planning G oals

A. Introduction

Under the Regional Haze Rule 5 , each State is required to demonstrate reasonable progress in visibility conditions for each of its Class I areas. The State is to determine a uniform rate of progress ("glide path", "glide slope") toward the goal of natural visibility conditions in 2064.

Cons idering various statutory factors, the State is also to define a reasonable rate of progress, and compare this to the benchmark uniform rate; if projected progress is less than the uniform rate, then the State is to explain why. Procedures for assessing p rogress are described in the Regional Haze Rule and EPA guidance documents.

In brief, the guidance defines a metric to quantify visibility conditions, together with procedures for determining a starting point and an ending point, between which progress is to be made. The metric used is the Haze Index, measured in deciviews, and is designed to correspond to human perception of visibility changes. It is defined as 10*ln(b ext /10), where b ext is extinction, the fraction of light scattered out of a sight path due to pollutants over a given distance (with units of

5 http://www.epa.gov/fedrgstr/EPA

- AIR/199 9/July/Day

- 01/a13941.pdf6 Mm - 1 or "inverse megameters"); it is inversely related to visual range. A 24

- hour average is used, so there is a deciview value for each day of the year; the average of the 20% most

- impaired days and of the 20% least

- impaired days are to be assessed. The CAA national visibility goal for

federal Class I areas is defined in the Regional Haze Rule , which is to improve visibility on the average of the worst 20% of days, while having no degradation on the average of the best 20%

. The national goal is to be achieved by 2064 with steady incremental progress in the interim

.

The starting point for progress is current or baseline visibility conditions, as monitored by the

I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE) monitoring network. Monitored pollutant concentrations are converted to visibility extinction using the original or the

revised IMPROVE equation, which adds up the contribution of each pollutant to extinction, while accounting for the effect of relative humidity; this is then converted to deciviews in the Haze Index. For each of the years 20 0 0

- 04 of the baseline period, the average of the deciviews on the worst 20% of days is calculated; the five

- year average of these defines the baseline. This procedure is described in detail in EPA's "Guidance for Tracking Progress Under the Regio nal Haze Rule" (hereafter “GTR” 6 ). The guidance also makes provision for dealing with missing data, since monitoring instrument maintenance and ma lfunctions mean that data is not available for every scheduled measurement.

The end point for progress is the goal of natural visibility conditions in 2064. The default approach for determining these is described in EPA's "Guidance for Estimating Natural Visibility Conditions Under the Regional Haz e Program" (hereafter “GENVC” 7 ). Starting with annual average natural background pollutant concentrations as estimated by Trijonis et al. (1990) 8 under NAPAP for the East and West parts of the country, deciview s are calculated with the IMPROVE equation, using the monthly relative humidity for each specific Class I area. These annual averages were then translated into estimates for the best 20% and worst 20% days needed for the progress assessment. Extinction w as assumed to have a lognormal frequency distribution; deciviews would then have a normal distribution, and its 10th and 90th percentiles were used as estimates of the average of the best 20% and worst 20% of days, respectively. The result is a

table of b est and worst 20% deciview values for each Class I area, which appears in Appendix B of the guidance. The guidance also allows States to use a refined alternative to this default approach for estimating natural conditions.

Finally, the uniform rate of pr ogress is the difference between the baseline and natural conditions, spread over the 60 years between 2004 and 2064: uniform deciviews per year improvement = (current 2004 deciviews

- natural 2064 deciviews) / 60. This rate is the benchmark against which visibility improvement is to be compared by the State; the first

6 EPA, 2003, Guidance for Tracking Progress Under the Regional Haze Rule , EPA

- 454/B

- 03

- 004, September 2003, EPA OAQPS; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html

direc t link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_tpurhr_gd.pdf

7 EPA, 2003, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program , EPA

454/B

- 03

- 005 , September 2003, EPA OAQPS ; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html

direct link: http://www.epa.gov/ttn/oarpg/t1/me moranda/rh_envcurhr_gd.pdf

8 Trijonis, J.C., et al., 1990, "Visibility: Existing and Historical Conditions

- Causes and Effects", chapter 24 in

NAPAP State of Science & T echnology, Vol. III; web page: http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm7 planning period envisaged by the Regional Haze Rule is through 2018, so this uniform rate is multiplied by 14 to determine the first benchmark.

B. WRAP P rocedures

The WRAP procedure for developing a uniform rate of progress (URP, also known as "glide path" or "glide slope") differed from the EPA default procedure in three principal ways: A. revised IMPROVE equation, B. alternative data substitution methods , and C. refined estimate of

natu ral conditions

The procedures used by the WRAP for determining the uniform rate of progress or "glide path" are consistent with those in the Regional Haze Rule and EPA guidance. They varied in some particulars, in order to address some issues raised abou t the EPA default approaches. These issues were thought to be especially important for the Class I areas in the western states comprising the WRAP, for which visibility is generally already fairly good, natural visibility

conditions are quite clean, and t he effects of wildland fire is not well represented in the EPA

default natural conditions estimates.

C. Revised IMPROVE Equation

WRAP used the revised IMPROVE equation for estimates of both baseline and natural conditions. The IMPROVE equation is use d to convert measured concentrations into extinction for each pollutant chemical species, and then total them up, accounting for the effect of relative humidity, and including the Rayleigh scattering that occurs in pure air. The extinction total is

then u sed to calculate deciviews for use in visibility progress assessments. I n December 2005 the IMPROVE Steering Committee revised the IMPROVE equation after a scientific assessment of its implications for regional haze planning. In particular, when compared to nephelometer direct measurements of visibility extinction, the original IMPROVE equation over

- predicts for

low extinction conditions and under

- predicts for high extinction. These biases have direct

relevance for estimates for the best 20% and worst 20 % visibility days that are used to assess

progress.

Old IMPROVE equation :

b ext = 3 * f(RH) * [ sulfate ] + 3 * f(RH) * [ nitrate ] + 4 * [ organic mass ] + 10 * [ elemental carbon ] + 1 * [ fine soil ] + 0.6 * [ coarse mass ]

+ 10

Each term in the equation is the extinction due to a particular measured component; bracketed quantities are concentrations of as measured at IMPROVE monitors. The organic mass is assumed to be 1.4 times the organic carbon mass that is measured by IMPROVE monitors. The

10 is for Ra yleigh scattering which is due to the interaction of light with molecules of air itself8 with no pollutants, and is assumed to be the same for all locations, The f(RH) is a water growth factor for sulfate and nitrate, which are hygros c opic (their particles tend to attract water). Its value depends on relative humidity, ranging from 1 at low humidity to 18 at 98% humidity.

New IMPROVE equation :

b ext = 2.2 * f

s (RH) * [small sulfate] + 4.8 * f L (RH) * [large sulfate]

+ 2.4 * f

s (RH) * [small nitrate] + 5.1 * f L (RH) * [large nitrate] + 2.8 * [small organic mass] + 6.1 * [large organic mass] + 10 * [ elemental carbon ] + 1 * [ fine soil ]

+ 1.7 * f

ss (RH) * [sea salt] + 0.6 * [ coarse mass ] + Rayleigh scattering (site

- specific) + 0.33 * [NO 2 (ppb)]

Sulfate is assume d to be all “large sulfate” if total sulfate is over 20 μg/m 3 , otherwise its fraction of the total is assumed to increase uniformly between 0 and 1 when the total is in the range between 0 and 20. I.e. large sulfate = (total sulfate/20)*total. A similar definition applies for nitrate and for organic mass. The organic mass is assumed to be 1.8 times the organic carbon mass that is measured by IMPROVE monitors, an increase over the old 1.4. Sea salt is estimated as 1.8 * [chloride] (or chlorine if chlorid e not available)

. Finally, t he f

s , f L , f

ss are water growth factors for small (“S”) and large (“L”) fractions of sulfate and nitrate, and for sea salt (“SS”). Their values depend on relative humidity, ranging from 1 at low humidity to over 5 at 95%

humid ity.

The new equation has five changes: 1) greater completeness though the inclusion of sea salt, which can be important for coastal sites; 2) increased organic carbon mass estimate, based on more recent data for remote areas; 3) Rayleigh scattering using site

- specific elevation and temperature, a refinement over the older network

- wide constant; 4) separate estimates for small and large particles of visibility impacts and humidity

- dependent particle size growth rates, which

could affect estimates at the lo w and high ends; and 5) greater completeness though the inclusion

of NO2 (Pitchford, 2006) 9

.

The new equation shows broader scatter overall, but less bias in matching visibility measurements under high and low visibility conditions. That is, though it ha s a somewhat worse fit considering all the data, it has a better fit under visibility conditions most relevant to regional haze planning, the best and worst 20% of days. The looser overall fit can cause a slightly different set of days to be the ones chos en as the 20% worst, but the chemical species composition for such days is little changed (IMPROVE technical subcommittee for algorithm review, 2001, pp. 11

- 12), and so this makes little difference for assessing the contribution of

emission sources to curr ent conditions, and for projecting the effect of emission controls. The

9 Pitchford, Marc, 2006, "New IMPROVE algorithm for estimating light extinction approved for use", The

IMPROVE Newsletter , Volume 14, Number 4, Air Resour ce Specialists, Inc.; web page: http://vista.cira.colostate.edu/improve/Publications/news_letters.htm

direct link: http://vista.cira.colostate.edu/improve/Publications/NewsLetters/IMPNews4thQtr2005.pdf9 split between small and large particles was the main factor in reducing the biases. Another effect of choosing a revised equation with broader scatter of nephelometer measurements (t ypically

resolved to ~5

- minute average data) versus IMPROVE particle sampling (24

- hour averages every 3 rd

- day) is that a typical viewer at a Class I area would be more likely to notice a difference between the visibility condition from the observed nephelo meter value and the calculated IMPROVE value, although not critical to the regional haze planning process specified

in the RHR.

The organic carbon (OC) measured by the IMPROVE network does not include all organic matter (OM); based on 1970s urban data, a scaling factor of 1.4 is embedded in the old equation to account for the full mass. Based on recent data more relevant to relatively remote Class I areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. In practice, for the worst days the big gest effect of switching to the revised IMPROVE equation is this increased organic carbon mass, since the worst days are dominated by organic carbon from fires, rather

than the sulfates and nitrates that come mo re from anthropogenic sources.

Review Commen t: The revised IMPROVE equation has less bias, is more refined, accounts for more pollutants, incorporates more recent data, and is based on considerations of relevance for the calculations needed for assessing progress under the RHR. EPA believes it is appropriate for the WRAP States to use the revised IMPROVE equation.

D. Baseline Visibility Conditions

EPA's "Guidance for Tracking Progress Under the Regional Haze Rule" ("GTP") describes a lengthy and detailed step

- by

- step process for calculating th e visibility metric to be used in tracking progress, including that for the baseline period 2000

- 2004 (GTP, sec. 2). The steps involve assembly of daily species concentration data from the IMPROVE network, inclusion of substitutions for missing data; asse ssment of site data completeness; calculation of extinction via the IMPROVE equation and then the deciview Haze Index; calculation of average deciviews for the 20% best and 20% worst days; averaging these over the 5 year period. These steps are mostly str aightforward and were handled by the IMPROVE monitoring program, with the results displayed on the WRAP technical websites. The IMPROVE program to track Regional Haze at 110 sites across the country to represent the national total of 156 Class I areas is funded by EPA and operated by the IMPROVE Steering Committee. The Committee is responsible for the

documentation of the IMPROVE data and the calculated RHR 5

- year averages . The main purpose of the WRAP documentation was to identify the differences bet ween EPA guidance and WRAP procedures, which involved substitution for missing data beyond those in the “GTP” procedures

.

For data substitution, the EPA guidance describes two procedures. The first is the use of quarterly median concentrations. This is the median concentration from the quarter the data value is missing from, averaged with similar medians from the preceding four years, using only quarters with having at least 50% of the days available and no more than 10 consecutive missing days (GTP, "S tep 3: Determine Quarterly Median Concentrations for Missing Variables", p.2

- 5). The second substitution involves using quarterly averages, as long as the substitution changes10 extinction values by less than 10% in 90% of the data (GRP, "Step 5

- Evaluate Feasibility of Substituting Average Values", pp. 2

- 7

- 2

- 8).

Completeness requirements are also given in the EPA guidance: to be complete and included in the progress calculation, a year must have data for 50% of the sampling days in every quarter,

75% of the days for the year overall, and no more than 10 consecutive missing sampling days;

the overall 5

- year period must have at least 3 complete years of data (GTP, "Step 7

- Check Data Completeness", pp. 2

- 8

- 2

- 9).

In order to meet the completeness requir ement for IMPROVE 13 sites that either did not have 3 complete years, or that were missing the year 2002 (needed for applying modeled relative response factors or RRFs to predict future conditions), WRAP used two additional procedures,

carbon substitution and donor sites (WRAP, 2007) 10 . Under carbon substitution, WRAP used hydrogen as a surrogate for missing carbon, based on the historical relationship between carbon and hydrogen. The relationship was a quarterly regression between organic carbon (OC) and the fraction of hydrogen assumed to be associated with organic carbon (measured hydrogen, less 24% of the measured sulfur to reflect hydrogen in inorganic compounds such as ammonium sulfate). In turn, elemental carbon (EC) was estimated from a quarterly r egression with OC.

A second substitution method was the use of donor sites. For missing species, correlations between the missing data site and nearby candidate sites were calculation, and then donor sites

selected in consultation with the State. Despit e these two additional substitution methods, data did not meet the completeness requirements for four sites in 2000, and one site in 2001. However, at all sites data was considered complete for 2002, and also met the ov erall completeness requirements for the five

- year period 2000

- 2004.

Review Comment: The WRAP used IMPROVE program

- calculated data which followed EPA guidance for estimating baseline visibility conditions, but did use two additional data

substitution methods. EPA believes the additional WR AP methods are reasonable extensions to the EPA approach, appropriate for the WRAP States to use, especially in view of the importance of the year 2002 for projecting future visibility conditions via modeling.

E. Natural Visibility Conditions

EPA guida nce set out a default procedure for estimating natural conditions, but also describes circumstances when States might want to use a more refined approach, such as to reduce uncertainty when baseline visibility is already near natural conditions, or when th ere is marked seasonality; these might be accomplished via alternative estimates of natural concentrations, or

use o f temporally varying estimates (GENV sec. 3.1 and 3.2).

A Natural Haze Levels II Committee ("NH2C") was established with participation of s everal Regional Planning Organizations (RPOs) that are responsible for regional haze planning. This committee recommended a refined approach for estimating natural visibility conditions, which

10 "WRAP IMPROVE Data Substitutions", April 3, 2007; web page: http://vista.cira.colostate.edu/TSS/Results/Monitoring.aspx

direct link http://vista.cira.colostate.edu/docs/wrap/Monitoring/ WRAP_Data_Substitution_Methods_April_2007.doc11 was used by the WRAP (Inter

- RPO Natural Haze Levels II Committ ee, 2006 ; hereafter “NH2C report” ) 11 . The natural conditions approach used the revised IMPROVE equation, so that progress between baseline conditions and natural conditions can be calculated on a consistent basis. In brief, the approach estimated the best and worst 20% day visibility using the distribution of measured actual concentration data to reflect temporal variation; however these data were scaled so that their annual average matched the Trijonis values in the EPA guidance.

As discussed by the NH2C (NH2C report, notes to slide 5), the EPA default approach assumed that extinction is lognormally distributed, as is the case for the distributions of concentration for many pollutants. Since the deciview Haze Index is based on the logarithm of extinction , this implies that deciviews are normally distributed. Also, empirically this assumption looks reasonable (GENV sec 2.6). However, since Rayleigh scattering is essentially constant for a given monitoring site, and is thus a constant term in the IMPROVE equation for extinction, its logarithm cannot be normally distributed. This is important for natural conditions, when the Rayleigh term will be a substantial fraction of the total extinction.

This constant Rayleigh term was also included in calculations of mean and standard deviation of deciviews for each site. On scatter plots of standard deviation against mean using data from multiple sites, this led to an apparent decrease of standard deviation as mean decreases. Extrapolating down to a mean equal to the annual deciviews derived from the Trijonis/NAPAP natural concentrations led to a corresponding natural standard deviation, which was 2 deciviews for Western sites. However, for the least hazy sites, this decrease with mean was mainly due to the Rayle igh term. So, including the latter distorted the estimate of standard deviation, which in turn distorted the estimates of the 10th and 90th percentiles (NH2C report, notes to slide 5).

A further assumption in the EPA approach was that the average of the normally distributed deciviews of the 20% best or worst days can be respectively represented by the 10th or 90th percentile. (These are 1.28 standard deviations below or above the mean, respectively.) Actually the 8th and 92nd are better estimators; but since the distribution is not normal, this criticism is less relevant (NH2C report, notes to slide 5).

To address these issues, the NH2C developed a refined approach (NH2C report, notes to slide 6; Copeland et al., 2008 12

11 "Natural Haze Levels II: Application of the New IMPROVE Algorithm to Natural Species Concentrations Estimates; Final Report by the Natural Haze Levels II Committee to the RPO Monitoring/Data Analysis Workgro up", presentation at WRAP Attribution of Haze Workgroup Meeting, July 26

- 27, 2006, Denver, CO. web

page: ) that did not depend on any statis tical assumptions. Instead, actual monitored concentrations were used as the basis for the best 20% and worst 20% deciview days. For each monitoring site and each chemical species, actual IMPROVE concentrations were scaled so that the annual average was equal to Trijonis/NAPAP natural concentrations of the EPA default approach. These "Trijonis

- adjusted" concentrations reflect the observed relative temporal variation of each IMPROVE site, yet are consistent with the natural concentrations in the EPA guida nce. For each day, the adjusted concentrations were used in the revised

http://vista.cira.colostate.edu/improve/Publications/Gray Lit/gray_literature.htm direct link: http://vista.cira.colostate.edu/improve/Publications/GrayLit/029_NaturalCondII/naturalhazelevel sIIreport.ppt

12
http://vista.cira.colostate.edu/improve/publications/graylit/032_NaturalCondIIpaper/Copel and_etal_NaturalConditio nsII_Description.pdf12 IMPROVE equation to calculate extinction, and converted to deciviews. The respective averages of the 20% best and the 20% worst days' deciviews was then calculated directly,

without assumptions on the statistical distributions as were needed in the EPA approach to translate extrapolated standard deviations into percentiles. Sites with annual average concentrations already below the Trijonis levels were not scaled upward, as that woul d imply current visibility would worsen in the future despite emission reductions. This procedure is consistent with the refined "sample

- period

- by

- sample

- period basis" described in EPA guidance (GENV sec. 3.6).

The procedure used has several acknowledged limitations. One it shares with the EPA approach: each chemical species can have one of only two possible background concentrations, one for the East and the one for the West. Future efforts may provide for a larger number of geographic

zones with diffe ring concentrations. Further, natural conditions are a flux across the ~120 IMPROVE sample days per year; the daily/seasonal variation will need to be addressed as well.

Two other limitations were discussed by the NH2C along with alternative procedures t o address them. First, the split between small and large particles in the revised IMPROVE equation depends on a relationship to concentration that is based on today's conditions. With future emission reductions, concentrations will be lower, and the rela tionship may change. The NH2C decided to avoid the significant effort and time needed to modify and validate changes to the IMPROVE equation that this would entail. In addition, future lower concentrations will likely involve relatively more particles in the smaller sizes already accommodated in the revised IMPROVE equation (NH2C report, notes to slide 6). The performance of the IMPROVE equation will need to be assessed over time as emission reductions occur.

A second potential limitation is that the sa me approach is used for both natural

- and anthropogenic

- dominated species components; EPA guidance mentions the possibility of treating these separately (GENV sec. 3.4). For the most part the variations considered by the NH2C kept

some or all assumed natu ral species components constant, e.g. carbon assumed to come from natural fires. However, these alternative assumptions were rejected as not valid for all sites, and/or requiring more site

- specific analysis than time permitted or than was warranted given the overall uncertainties (NH2C report, notes to slide 6). This, too, is a potential area for future

efforts.

Review Comment: EPA guidance allows for a more refined approach to estimating natural visibility conditions (GENVC 3.1, 3.3) as was used here. This is especially appropriate for areas in the West, some of which already have fairly good visibility and are affected episodically at a high magnitude by natural sources, e.g., wildfire . The method used by the WRAP matches EPA's general approach, but is more refined and does not rely on some of the statistical assumptions used in the default EPA procedure.

Review Comment: The approach used to estimate natural visibility seems superior to EPA default approach in a number of ways, and EPA believes it i s appropriate for the WRAP States to

use.



### 3. Emissions Inventories13 This WRAP emissions inventory work presented here was divided into three broad categories: point and area source projections developed by ERG, fire emissions inventory developed by Air

Sci ences and mobile source emissions inventories developed by E NVIRON . This chapter reviews the methodologies behind the WRAP analysis of each of these broad source categories to determine if the they are consistent with applicable EPA guidance and are theref ore likely to be accurate, current and complete.

T he overall emissions inventory work by the WRAP dealt with a very large array of sources and magnitudes varying across the West. K ey anthropogenic source categories that must be addressed in the Regio n al Haze planning effort are discussed in this Chapter (e.g., point, stationary area

, mobile, road dust, prescribed and agricultural fire). Discussion of source s that

are defined as natural

, quasi

- natural , and/or have limited or no controls possible (e.g., wi ldfire,

dust in general

, ammonia, et cetera) and source categories mostly out of the control of the states (commercial marine, transport from other countries, et cetera ) are als o presented in a very limited fashion, prioritized to review of controls addres sed in Regional Haze SIPs . Emissions from all

of No r th America (other 4 RPO regions, Mexico, and Canada ) were also used in the Regional Modeling Center scenarios (both historic and 2018 projections) , but are not addressed in this

section. Additional sour ce categories (e.g., oil & gas area sources, biogenics, fugitive dust, windblown dust, et cetera) are not discussed.

A. Point and Area Sources Projections

The WRAP technical work for this category may be found in the Final Technical Memorandum

Final dev eloped by ERG Titled “ WRAP PRP18b Emissions Inventory

– Revised Point and Area Sources Projections ” revised October 16, 2009. The purpose of this evaluation is to determine if the WRAP Inventory meets the CAA requirement for a comprehensive, accurate and current inventory and can be used by States and Tribes for inclusion in their Regional Haze State Implementation Plans. The WRAP Inventory provides the most significant stationary point and

area source visibility

- impairing pollution within each of the fol lowing States: Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming, plus tribal sources within these States.

Scope of the Point and Area Source Projections

The Te chnical Memorandum identifies all stationary point and area sources located with the WRAP states of Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming; as well as tri bal sources within those states. Certain area sources are excluded because they are being dealt with by other contractor teams: (i.e., fugitive dust, fires, area ammonia sources, and on and off road mobile sources.). Updates to the oil and gas (O&G) area s ource emissions, and O&G point source SO 2 emissions were developed by ENVIRON under another project, and provided to ERG for inclusion with the other point and area sources in the WRAP PRP18 emissions inventory. The

California emissions were not modified i n any way, and were simply retained as

- is from the previous WRAP 2018 base case version 1 inventory.14 In some limited cases, changes were also made to the 2002 baseline inventory when these changes were straightforward and did not affect emission values (e .g., correction of missing or erroneous source classification codes [SCCs], changing facility names to be consistent across records, etc.). In only a few instances were records added to the 2002 inventory, and subsequently, were projected to 2018 and added to the WRAP PRP18 inventory. Additional 2002 changes were identified, but were deferred until the next update of these 2002 and 2018

WRAP inventories.

The following categories were NOT captured by the Technical Memorandum Developed by ERG: Fugitive dust, fires (natural, prescribed, and managed wildfire), area ammonia sources, and

on and off road mobile sources.

Point and Area Source Methodology

ERG updated the existing WRAP 2018 version 1 database to be consistent with the 2018b “base

case” emissions in ventory used by the WRAP Regional Modeling Center (RMC) for the 2018 base case modeling analysis completed in June 2006. These changes are itemized in the RMC 2006 final report, Appendix A. After discussion with the RMC, and comparison to the 2018b

base case emissions inventory, a few other changes were necessary to make the 2018 version 1 inventory consistent with the 2018b base case inventory, including the following:

**•** Added NO x and CO emissions for four new (post

- 2002) compressor stations in ND;

**•** Added SO 2 emissions for 4 facilities in WY; and

**•** Added NO x , VOC, and CO emissions for 7 compressor stations in WY.

Using the updated 2018b base case emissions database, spreadsheets were developed to show the top 80% of emissions of point source and area source emissions, by SCC, for each state. Also, spreadsheet entitled “Proposed Changes & Feedback Request” itemized other potential changes that might need to be made to each jurisdiction’s 2018b base case inventory to update it for PRP18 purposes. The types of changes proposed and feedback requested from each state and local (S/L) agency included the following:

**•** Confirmation of changes in SO 2 emissions from major sources located in §309 states (i.e., AZ, NM, UT, and WY);

**•** Request for BART emission limits;

**•** Request for information on any new post

- 2004 “on

- the

- books” controls or permit limits;

**•** Request to provide missing SCCs (and itemized list of records with missing SCCs was provided); and

**•** Confirmation that all dual

- fueled sources were accounted for in the emissions inventory. These spreadsheets were e

- mailed to each S/L agency for review prior to holding conference calls to explain the spreadsheets and receive initial comments from the agencies.

The basis of the WRAP 2002 baseline emissions inventory, and thus the WRAP 2018 version 1 projections, were the data submitted by state and local (S/L) agencies and tribes for the U.S. EPA National Emissions Inventory (NEI). During the development of the NEI, ERG (as an U.S. EPA contractor) was requested to use NO x and SO 2 e missions data for subject EGUs from the U.S.15 EPA Clean Air Markets Division (CAMD) database, instead of the emissions submitted by the S/L agencies for those EGUs reporting to CAMD. When these NO x and SO 2 emissions data were placed into the NEI, they were assigned a unique facility ID beginning with “EGU” in order to distinguish that record from the record submitted by the state. Generally, the NO x and SO 2 emissions values from CAMD varied only slightly from those submitted by the S/L agency (i.e.,

ERG obse rved results showing about a 1

- 2% difference in emissions between CAMD and S/L agency emissions). However, the use of the CAMD “EGU” numbering scheme caused there to be two different facility IDs for the same EGU in the resulting dataset. This eventually b ecame recognized as a problem when these data were used in the WRAP 2002 and 2018 pivot tables and in the WRAP Emissions Data Management System (EDMS). These issues were corrected in

the final memorandum.

Review comments: EPA finds that the methodologies used to develop the stationary and area sources inventory is consistent with EPA’s guidance document titled: “Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter national Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations.” Dated August 2005.

B. Fire Emission Inventories

The technical work for this category may be found in the document: Development of 200

- 04 Baseline period and 2018 Projection Year Emission Inventories, FINAL dated May 2007 by Air

Sciences I nc.

Scope and Methodology of the Fire Emission Inventories

The Fire Emissions Joint Forum (FEJF) of the Western Regional Air Partnership (WRAP) completed an air emission inventory for fire in 14 of the 15 states in the WRAP region. (Hawaii

is not in th e inventory.) The inventory includes emission estimates and activity data for wildfire, prescribed fire, wildland fire use (WFU), agricultural burning, and non

- federal prescribed rangeland (NF rangeland) burning for the calendar year 2002. The FEJF collect ed fire activity data from federal, tribal, and state agencies; arrived at data quality objectives; culled data from the database that did not meet the data quality objectives; allocated summary data to realistic fire events where necessary; devised emissi on calculation routines; estimated emissions for all fire events; and published an emission inventory database and dispersion model

- ready digital files.

Many of the techniques utilized for developing the fire emission inventory are based on the

WRAP techn ical report entitled “1996 Fire Emission Inventory.” The FEJF produced an event

based emission inventory, placing all fire emissions at coordinate locations on specific days. Federal and state records of individual fire events were collected. For agricultu ral and nonfederal rangeland burning, county level data on a monthly basis was collected and allocated to the coordinate level on a daily basis for jurisdictions where event level data was not available. In general, burning activity data was not available directly from tribal agencies. Federal land manager (especially Department of Interior

– Bureau of Indian Affairs) data included in the federal databases as well as state data may include burning in Indian Country. Quality Control

Packets whe re sent to sta tes, Tribes, and Federal Land M anagement agencies (FLMs) to solicit corrections to the 2002 activity data for wildland burning and agricultural burning.16 T welve pollutants are included in the fire inventory. They are: total suspended particulate matter (T SP), particulate matter less than 10 microns in diameter (PM 10 ), PM 2.5 , elemental carbon (EC), organic carbon (OC), non

- methane volatile organic compounds (VOC), methane (CH 4 ), ammonia (NH 3 ), oxides of nitrogen (NO

x ), carbon monoxide (CO), sulfur dioxide ( SO 2 ), and coarse particulate matter defined as the difference between PM 10 and PM 2.5 (PM
coarse ). Activity records were used and checked for completeness for fire size, fuel loading, date, and location. Activity records deemed incomplete and therefore no t useable in an emission calculation were culled from the database (and retained in a companion database for documentation purposes). Fuel loading and emission factor tables along with diurnal consumption and plume profiles were developed from the literat ure, expert and professional judgment, and stakeholder input. Spreadsheet and geographic information system software was used to store the data, perform data augmentation and quality control functions, calculate emissions, and produce the strictly

formatt ed National Emission Inventory Format (NIF) 3.0 and SMOKE/IDA text export files of

the inventories . The fire inventory was passed to the Regional Modeling Center as PM2.5, PM10, NOx and CO emissions, and the RMC applied chemical profiles to derive fully s peciated and gridded fire emissions data. The fire inventory was provided to the RMC with a lookup

table for effective plume rise based on acreage burned per day, and a lookup table for diurnal emission profile and corresponding smoldering at the end of t he fire event.

Limitations of the Fire Emission Inventory

Limitations of the fire emission inventory include the omission of fire events (e.g., tribal burning,

in general, not accounted for in non

- tribal data sets and individual fire activity records de emed to be incomplete) and variable data quality due to the variety of data sources used.

Estimating emissions from fire events involves considerable scientific uncertainty. Historic data are of varying quality and for some areas unavailable. Activity re cords were not ground

- truthed and, other than quality control steps described in Air Sciences report , were generally accepted

“as is.” Parameters such as the vegetation type of a burn, the vegetation

- specific fuel loading, pollutant specific emission facto rs, and combustion efficiencies, to name a few, all have

uncertainties associated with them which may influence emission estimates and regional modeling results. Fire has been traditionally treated as an “area” source, but in the WRAP’s regional dispersio n model , it is treated as a point source. Therefore, fire emissions were placed

at a latitude/longitude coordinate location for each day. From the daily and spatially resolved emission inventory, hourly consumption and plume rise were estimated. Finall y, the inventory

states that professional judgment was used to select the best available or most representative parameters or methods to estimate emissions. However, other parameters and methods could

have been chosen and could also be considered “reasonable ” for estimating emissions from fire.

Review comments: EPA finds that the methodologies used to develop the fire sources inventory is consistent with EPA’s guidance document titled: “Emissions Inventory Guidance for Implementation of Ozone and Particulat e Matter national Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations.” Dated August 2005.

C. Mobile Source Inventories17 The technical work for these source categories may be found in the document titled: Final Report, WRAP Mobile Sour ce Emission Inventories Update, Dated, May 2006, prepared by

ENVIRON. The purpose of this evaluation is to determine if the methodologies used to develop the Mobile Source Emission Inventories are adequate for a Regional Haze SIP.

Scope of the Mobile Source Inventories

E NVIRON has produced methodologies for the following mobile sources: On

- Road, Off Road, Locomotive, Aircraft, Commercial Marine, and Road Dust.

The scopes of these inventories are as follows:

Geographic domain: Emissions were esti mated by county for all counties in the following 14 states: Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, a nd Wyoming. Hawaii was not included.

Temporal resolution: Emi ssions were estimated for an average day in each of the four seasons, and for an average annual weekday. Seasons are defined as three

- month periods: spring is March through May; summer is June through August; fall is September through November; and

winter is December through February. Emission s were estimated for the 2002 base year and for three future years

– 2008, 2013, and 2018.

Pollutants: Emissions were estimated for primary Particulate Matter (PM10 and 2.5), Nitrogen Oxides (NOx), Sulfur Dioxide ( SO2), Volatile Organic Compounds (VOC’s), Carbon Monoxide (CO), Ammonia (NH3), Elemental and Organic Carbon(EC/OC), and sulfate (SO4).

Sources: For all pollutants, emissions were estimated separately by vehicle class for on

- road sources and by equipment type/engine type for off

- road sources. Emissions were summarized for gasoline and diesel

- fueled engines.

Mobile Source Inventory Methodologies

As with most emissions sources, on

- road and off

- road mobile source emissions are estimated as the products of emission factors and activity estimates. With the e xcept ion of California, all on

road mobile sources emission factors were derived from EPA’s MOBILE6 13 model

. Activity for

on

- road mobile sources is vehicle miles traveled stated as VMT. State and loca l agencies were provided default modeling inputs and VMT levels for base and future years for review and update; all states and several agencies provided updated. The California Air Resources Board (CARB) provided on

- road emissions estimates by county and vehicle class directly; these were

based on CARB’s in

- house version of their EMFAC model for the State of California

.

13 EPA’s MOBILE6 model is available at http://www.epa.gov/OMSWWW/m6.htm

.18 For all states except California, EPA’s draft NONROAD2004 model was used to estimate so

called traditional off

- road sources 14 , all source s listed above except aircraft, commercial marine, and locomotives. The NONROAD model includes estimates of emission factors, activity levels, and growth factors for all traditional off

- road sources. The default activity levels were provided
to state age ncies for input and update; however, no state provided updated off

- road activity data. Emissions estimation methods for aircraft, commercial marine, and locomotives were similar to approaches EPA has recently used in developing national emission inventori es. For California, CARB provided off

- road emissions estimates by source category and county directly.

Here are the methodologies used to create the mobile source inventories:

On

- Road :

T he emissions model inputs and calculations performed for estimati ng the 2002 base year and 2008/2013/2018 future year on

- road emissions us ed EPA’s MOBILE6 model. The generation of SMOKE files for input into the air quality model was also used . To estimate on

- road emissions,

defaults were established for all mobile sou rce model input parameters, and also for VMT estimates. These default inputs were sent to state and local air quality planning agencies in the WRAP states (except for California), and they were requested to provide the most up

- to

- date modeling and VMT inp uts.

Updated data and model inputs provided by state and local contacts are shown below. All state agencies responded to the survey requests for base and future year modeling inputs. In addition, survey responses were received from the local agencies for the following counties:

**•** Arizona

– Pima and Maricopa

**•** Colorado

– Denver nonattainment area counties

**•** Nevada

– Clark and Washoe

**•** New Mexico

– Bernalillo

For California, the Air Resources Board provided emissions estimates directly for all years,

estimat ed using an internal version of the EMFAC2002 model, with updated activity data for

some areas.

The default VMT data used as the starting point in this analysis were the annual VMT estimates that EPA had compiled for the 2002 National Emission Inventory (NEI2002), in the county database for the National Mobile Inventory Model 15

14 The final version of NONROAD (NONROAD2005, available at (NMIM). EPA had derived the VMT estimates from Highway Performance Monitoring System (HPMS) data from the Federal Highway Administration (FHWA). State and local data provided to EPA as part of the June 2004 Consolidated Emissions Reporting Rule (CERR) submittals were incorporated into the default VMT estimates. These default VMT estimates, by county, vehicle type, and roadway type, were posted to the WRAP Mobile Sources Update P roject web page, and a survey form

http://www.epa.gov/otaq/nonrdmdl.htm ) was released after the work in this project was completed.

15 National Mobile Inventory Model is available at http://www.epa.gov/OMSWWW/nmim.htm

.19 was sent to state and local air quality planners asking them to review the estimates and provide updates where available. By default the annual VMT were assumed to be allocated evenly to each season; two states (WA and U T) had provided seasonal VMT allocation data that was included in the default modeling inputs files posted.

Most of the state and local agencies that responded to the survey provided updated VMT estimates. The areas for which updates were not provided and the state or local agency accepted

the NMIM defaults were: Arizona

- all counties except Maricopa; Denver area; North Dakota; South Dakota; and Washington. In most of these cases, the VMT estimates had been previously provided to EPA under the CERR an d had already been incorporated into the NMIM database.

Off Road :

This section describes the methods for estimating 2002 base year and 2008/2013/2018 future year emissions for so

- called traditional off

- road equipment. Equipment types included here are

i n the following categories:

**•** airport ground support, such as terminal tractors;

**•** agricultural equipment, such as tractors, combines, and balers;

**•** construction equipment, such as graders and back hoes;

**•** industrial and commercial equipment, such as fork lifts a nd sweepers;

**•** recreational vehicles, such as all

- terrain vehicles and off

- road motorcycles; residential and commercial lawn and garden equipment, such as leaf and snow blowers;

**•** logging equipment, such as shredders and large chain saws;

**•** recreational marine vessels, such as power boats;

**•** underground mining equipment; and

**•** oil field equipment.

Seasonal average daily emissions were estimated for the thirteen non

- California western states using EPA’s NONROAD model. To estimate emissions from these sources, defaul ts were established for all NONROAD model input parameters. These default inputs were sent to state and local air quality planning agencies in the WRAP states (except for California), and they were requested to provide the most up

- to

- date modeling. For C alifornia, the Air Resources Board (CARB) provided all off

- road emissions estimates from their own modeling system, which has similar equipment types.

Locomotive :

County level locomotive emissions estimates were estimated as the product of locomotive f uel consumption and average locomotive emission factors. Previous WRAP locomotive emissions estimates (Pollack et al., 2004) allocated national fuel consumption estimates to counties using emissions data offered by the National Emissions Inventory. For t his project a detailed revision to that allocation method was developed for allocating 2002 national fuel consumption estimates.

Emission factors were also revised to combine line

- haul and switching engines because only national total fuel consumption was available. Additional emission factors for ammonia and fuel20 sulfur provided by EPA were also incorporated and form the basis from which sulfur dioxide

was estimated.

Aircraft :

County

- level aircraft emissions for 2002 for the WRAP states were obtained f rom work performed for EPA’s 2002 National Emissions Inventory (NEI2002). Activity data for aircraft emissions are takeoff cycles (LTOs), and emission factors are primarily from the Federal Aviation Administration (FAA) Emissions and Dispersion Modeling S ystem (EDMS). The 2002 emissions were projected to future years using forecast LTOs available from the FAA. More detailed estimates were provided for some states.

The FAA EDMS model combines specified aircraft and activity levels with default emissions factors in order to estimate annual inventories for a specific airport. Aircraft activity levels in EDMS are expressed in terms of LTOs, which consist of the four aircraft operating modes: taxi and queue, take

- off, climb

- out, and landing. Default value s for the amount of time a specific aircraft spends in each mode, or the time

- in

- modes (TIMs), are coded into EDMS.

Aircraft emissions were estimated for four aircraft categories:

**•** Air carriers, which are larger turbine

- powered commercial aircraft with at least 60 seats or 18,000 lbs payload capacity;

**•** Air taxis, which are commercial turbine or piston

- powered aircraft with less than 60 seats or 18,000 lbs payload capacity;

**•** General aviation aircraft, which are small piston

- powered, non

- commercial aircraft; and

**•** Military aircraft.

Commercial Marine :

Commercial marine emissions comprise a wide variety of vessel types and uses. Table 3. 1 describes the different types of commercial marine vessel activity. In the previous WRAP

mobile sources emission inven tory work, emissions were estimated for most types of vessels (Pollack et al., 2004 16 ).

Table 3. 1. Commercial Vessel T ypes.

Source Definition Purpose Geographic Area

Deep draft Ocean

- going large

vessels Ocean Traffic Near port

Tow or Push Boats Bar ge Freight River Traffic

Ocean Traffic

Tugs Vessel assist and support functions Near port

16 Pollack, A.K., R. Chi, C. Lindhjem, C. Tran, P. Chandraker., P. Heirigs, L. Williams, S. S. Delaney, M. A. Mullen, and D. B. Thesing. 2004. “Development of WRAP MOBILE Source Emission Inventories.” Prepared for Western Governors’ Association, Denver, Co lorado.21 Ferries River or lake ferrying Regular routes

Other Commercial

Vessels Smaller support or

excursion boats Near dock

Dredges Dredging projects Varies

Commercia l Fishing Market fishing Ocean

Military Coast Guard and Navy Ocean & Port

For this inventory , emissions were estimated for deep draft vessels within shore and near port using port call data, and offshore emissions generated from ship location data. The most important revision for commercial marine emissions leading to regional haze (PM, SOx, and NOx) was the estimation of emissions for the offshore activity, primarily of ocean

- going vessels. This activity was not previously estimated for the WRAP emiss ion inventory, and has been a subject of concern as vessel traffic passes out from and along and upwind of the western coast of the US. The other revision conducted here was to update in

- shore deep

- draft vessel emissions to reflect changing fleet mix, esp ecially the retirement of steamship powered vessels. One issue for modelers was which vertical grid layer to introduce the deep draft emissions. The stack height of 34 to 58 me ters (Starcrest, 2004 17 ) and plume rise for ocean

- going (deep draft) vessels indicated that the emissions should be placed in the second vertical layer (above 36 and below 73 meters). The plume rise was estimated at 2 meters using standard plume rise

model s with the vessel speed of 17 to 25 knots as the wind speed, exhaust exit rate of 35 to 40 meters per second with an average stack diamet er of about 1.3 meters 18

.

Road Dust:

In the previous WRAP mobile source emissions inventory work, fugitive road dust e missions for unpaved roads were revised from the traditional EPA estimates with updated silt loading values, updated and revised activity estimates, and the application of transport fractions (Pollack et al., 2004). The aim of that work was to resolve lar ge differences in road dust emissions in adjacent counties, and to use a consistent methodology across the WRAP region. The revised road dust emissions were estimated for 1996, the base year for the original WRAP modeling work, and

2018.

For this inven tory , paved and unpaved road dust emissions were updated using the updated VMT for the base and future years provided by state and local contacts as part of the base and future year survey work. Any updated road dust controls provided were also incorporat ed into the

estimates.

Road dust emissions in the previous work included application of a factor to account for deposition and other removal mechanisms that tend to lower the amount of dust that is transported on a regional basis (i.e., across the 36 km grid cells in the WRAP modeling domain). The county

- specific transport fractions that were applied depend on the vegetative characteristics

17 Port of Los Angeles. 2001 Baseline Emissions Inventory, prepared by Starcrest Consulting Group, LLC, June,

2004.

18 http://vista.cira.colostate.edu/docs/wrap/ emissions/OffshoreEmissions.doc22 of each county, and were calculated as the weighted average of vegetation

- specific transport

fractions in each co unty. For the current work, updated transport fractions were available, but were applied to the road dust emissions (and other dust sources) in the SMOKE emissions processing rather than in the development of the county

- level emissions.

Limitations of th e Methodologies

Commercial Marine :

This inventory did not consider ocean

- going vessels heading to and from Vancouver, B.C. and other Canadian traffic that may pass into or near US waters. The emissions from these vessels

were estimated out 25 miles from the Pacific Ocean coast and used to compare with the estimates from the method used to estimate emissions off the coast and in the open ocean. For Alaska, there are missing ports that are not included in the inventory such as Juneau , the Alaska State

cap ital. Juneau is locked out from motor vehicle access to the surrounding area and therefore relies upon marine shipping and transportation as well as air traffic for its goods and services. It is strongly advised that Alaska review the emissions carefully as there may be numerous omissions, Juneau being one of them. Military emissions were also not estimated because the activity data was not available to ENVIRON , and offshore emissions except those in the California emissions inventory were not included ( i

. e. off shore oil wells).

Road Dust Methodologies:

While this inventory was being developed , EPA’s guidance on estimating paved and unpaved road dust emissions was updated 19 . For this inventory , the road dust emissions only reflect updated VMT and contr ols supplied be State and local agencies , and do not reflect the updated EPA guidance methodology

Road dust emissions estimates in the earlier WRAP work did not include Alaska, as Alaska was

not a WRAP member at the time. Therefore, r oad dust emissions are not estimated for Alaska. For California, road dust emissions provided by CARB were used.

Review Comments: The methodologies used to determine the 2002 base year three future years inventory

– 2008, 2013, and 2018 are correct. EPA finds that the me thodologies used to develop the mobile source inventory are consistent with EPA’s guidance with the above mentioned

limitations.

D. Emissions Inventory Versions

WRAP developed several emissions inventories over the course of their technical work. This

se ction describes only the final versions of these inventories that were used to develop the 2018 visibility projections and one of the source apportionment analyses. The focus here is on the final

emissions inventories served as the basis for technical work that was then incorporated into state regional have plans.

19 see http://www.epa.gov/ttn/chief/ap42/ch13/index.html

.23 Two models were used in WRAP’s technical work. The Community Multiscale Air Quality Modeling System (CMAQ) was used to estimate visibility impairment at Class 1 areas in 2018. The Comprehensive Air Quality Model with extensions (CAMx) was used to quantify the relative contributions of sources of pollution causing visibility impairment at Class I areas in

2018. These two efforts are described in more detail later in this document.

Table 3.2 show s the final inventory versions used in these CAMx and CMAQ analyses. All of these inventories are complete, that is, they include all of the relevant point, area and mobile sources of pollution that have been shown to contribute visibility impairment.

Ta ble 3.2

– Inventories Used in WRAP Modeling Purpose CMAQ Model CAMx Model Model performance evaluation Base02b Base02b 2018 Visibility Projections Plan02d PRP 18b Particulate Source Apportionment Technology Plan02c Base18b

The first step in mode ling is to evaluate the performance of the model to verify that it is representative of real world conditions. WRAP selected particular days where the ambient concentrations of pollutants at Class I areas are known and evaluated how well the model predicts those concentrations. One of the inputs to this model performance evaluation is an emissions inventory that accurately reflects actual emissions on the days in question. The WRAP used the Base02b inventory to evaluate the performance of both models.

The Plan02 inventories are designed to be representative of baseline conditions before the Regional Haze plans are put into place. They are different from the Base02 inventory in that

multi

- year averages are used for some source categories that are highly vari able from year to year (e.g. wildfires and electric generating unit emissions). Additional refinements and data corrections were incorporated into the Plan02 inventories as they evolved in order to ensure the most accurate, representative 2002 inventories possible at the time of the modeling. Details on the inventories up until the Plan02c may be found in the WRAP summary document. 20 The differences between the Plan02c and Plan02d inventories may be found in the WRAP Regional Modeling Center specification f or Plan02d 21

.

The two 2018 inventories used by WRAP in their final work are the Base18b inventory used in the particulate source apportionment techno logy (PSAT) analysis and the PRP 18b model used to estimate 2018 visibility levels at Class I areas. The di f ferences between the PRP 18b and Base18b are discussed previously in this chapter. They are further detailed in the WRAP Regional Modeling Center specification for PRP 18b 22

.

Review Comment: It is reasonable and appropriate for WRAP to develop the Plan02 i nventories to better represent baseline conditions and to incorporate the latest data into their emissions

inventories.

20 “Emissions Modeling” at http://vista.cira.colostate.edu/docs/wrap/emissions/EmissionsOverview.doc

. 21 Plan02d specification at http://pah.cert.ucr.edu/aqm/308/spec_sheets/SpecSheet_Plan02d_11_03_2008.doc

22 Prb18b specification at http://pah.cert.ucr.edu/aqm/308/spec_sheets/SpecSheet_PRP18b_Aug11_2009final.doc24 The different versions of the 2002 and 2018 inventories used in the PSAT and 2018 visibility projections are not significant for the WR AP area as a whole. In the future, it would be better to use identical inventories so that there is no perceived inconsistency.

These emissions inventories are sufficiently complete, current and accurate for the purposes of inclusion in state regional ha ze plans.



### 4. Meteorological Modeling

Photochemical grid models, such as the Community Multiscale Air Quality Modeling System (CMAQ) and the Comprehensive Air quality Model with extensions (CAMx), require inputs of

three

- dimensional gridded meteorological da ta, including wind, temperature, humidity, cloud/precipitation, and boundary layer parameters. The Fifth

- Generation Penn State/NCAR

Mesoscale Model (MM5) was used to develop these input fields for the WRAP visibility modeling. MM5 is a state

- of

- the

- scien ce atmosphere model that has proven useful for air quality applications and has been used extensively in past local, state, regional, and national modeling efforts. MM5 has undergone extensive peer

- review, with all of its components continually undergoing development and scrutiny by the modeling community. In

- depth descriptions of

MM5 23 can be found in Dudhia (1993) 24 and Grell et al. (1994) 25

. The MM5 model is no longer being updated because new research efforts are focused on the Weather Research Forecast (WRF) model, however, MM5 was the state of the science model at the time the WRAP visibility modeling was completed, and MM5 continues to be used in air quality modeling

studies. All meteorological data used for the WRAP air quality modeling efforts are derived

from MM5 model simulations.

The SMOKE emissions processor also requires meteorological inputs derived from MM5, most notably for temperatures needed for mobile and biogenic emissions processing. MM5 derived

windfields were also used in the crea tion of emission inventories for wind

- driven emission

sources.

The WRAP formed the Regional Modeling Center (RMC), consisting of the University of California at Riverside (UCR), ENVIRON International Corporation, and the University of North Carolina (UNC) . The RMC completed all MM5 modeling necessary to support analysis for the Section 308 SIPs/TIPs. Initially, the RMC completed meteorology modeling for the entire year of 2002 on two grids: a continental

- scale domain with 36

- km grid spacing, and a regio nal

- scale domain with 12 km grid spacing covering the western Class I areas. After concluding that visibility modeling had acceptable performance at both grid resolutions, the 12

- km modeling was discontinued. Therefore, this document only discusses the d evelopment of meteorological inputs for the 36

- km domain.

23 http://www.mmm.ucar.edu/mm5

24 Dudhia, J., 1993. “A non

- hydrostatic version of the Penn State/NCAR Mesoscale Model: validation tests and simulation of an Atlantic cyclone and cold front.” Mon. Wea. Rev

. 121 , pp.1493

- 1513.

25 Grell, G.A., J. Dudhia, and D.R. Stauffer, 1994. “A description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5).” NCAR Technical Note, NCAR TN

- 398

- STR, 138 pp.25 In addition to development of meteorological inputs for CMAQ and CAMx, MM5 was also used to develop meteorological inputs for the CALMET/CALPUFF modeling system. A s discussed

further in Section 7 of this TSD, CALMET/CALPUFF was used to determine whether a BART eligible source contributes to visibility impairment at a C lass I area. Refer to Section 7 of this TSD for further information on the use of MM5 for BART modeling.

The WRAP meteorological modeling is described fully in the RMC report entitled A nnual 2002 MM5 Meteorological Modeling to Support Regional Haze Modeling of the Western United States (Kemball

- Cook et al., 2005) 26

.

A. Meteorological Modeling Protocol

The Revised Draft Protocol: 2002 Annual MM5 Simulations to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP (ENVIRON and UCR, 2004) 27 describes in detail the MM5 model and the setup and evaluation methods used by the RMC in the 2002 modeling effort. The Modeling Protocol provides a brief description of MM5, the WRAP modeling domain, the MM5 physical configuration, and model application. The Modeling Protocol also presents the plan for evaluating the performance of the model in replicating the evolution of observed winds, t emperature, humidity, and boundary layer morphology. The model performance evaluation serves as the primary approach to assess the reliability of the meteorological fields to adequately characterize the state of the atmosphere for input to CMAQ/CAMx. Key elements of the MM5 model configuration and evaluation are discussed briefly below.

B. Meteorological Modeling Domain and Vertical Layer Structure

In the WRAP 36

- km simulation , MM5 was configured to run on the standard continental

- scale Regional Planning O rganization (RPO) National Grid with 36

- km grid point spacing (Figure

4.1 ). The RPO National Grid is defined on a Lambert conformal projection, with true latitudes at 33°N and 45°N, and the central latitude and longitude at 40°N and 97°W, respectively. The grid point spacing is 36

- km. The continental expanse of this domain results in a grid of 165 (east

west) by 129 (north

- south) dot points, and 164 (east

- west) by 128 (north

- south) cross points. Overall, the domain covers 5904 km by 4608 km

. The MM5 domain provides overlap of the CMAQ or CAMx air quality modeling grid to alleviate any numerical boundary artifacts that may be present in the MM5 output fields.

The vertical layer structure of the WRAP domain consists of 34 layers, a top level at 100

millibars , and increasing layer thickness with altitude. The vertical layer structure is further detailed in the Modeling Protocol.

26 Kemball

- Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2005. Annual 2002 MM5 Meteorological Mo deling to Support Regional Haze Modeling of the Western United States.” Draft Final Report. ENVIRON International Corporation and UC Riverside. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml

27 ENVIRON and UCR. 2004. “2002 Annual MM5 Simulations to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP.” Draft Protocol. ENVIRON International Corporation and the University of California at Riverside. April. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml26 Figure 4.1. RPO National 36

- km MM5 Grid

C. Model Configuration

The final WRAP MM5 modeling system configuration for the 2002 annua l simulation is provided in the Modeling Protocol. RMC conducted an initial simulation with a configuration taken from earlier MM5 simulations by the State of Iowa and LADCO. Then, further sensitivity tests were made to identify an MM5 configuration that would address performance issues

identified from the initial annual run.

The initial 2002 36

- km WRAP simulation results showed that MM5 performed better in the Central and Eastern U.S. than in the West, and performed generally better in winter than in

sum mer (Kemball

- Cook et al., 2005) 26 . In the western U.S., the amplitude of the diurnal temperature cycle was persistently underestimated during the summer, especially in the southwest. In the desert southwest, the humidity was greatly overestimated during th e summer as well, and there was a pronounced cold bias. Some of these problems appeared to be linked to the excessive simulated precipitation generated by MM5 during the summer, especially in the southwest. This can have serious repercussions for CMAQ mode ling since too much rain can “wash out” pollutants, while the too cool, humid and cloudy environment may lead to incorrect pollutant chemistry and aerosol thermodynamics. Wind performance improved quickly with height above the surface, suggesting that regi onal transport speeds/directions were reasonably represented.27 A number of sensitivity tests were performed by the RMC to resolve the poor performance issues identified in the initial simulation. In particular, RMC performed sensitivity tests related to four dimensional data assimilation (i.e., grid or analysis nudging), as well as for a number of physics options associated with cumulus param et erization , cloud microphysics, land surface models, and planetary boundary layer models.

The result of sensit ivity tests was a revised MM5 model configuration that showed:

**•** A dramatic reduction in the summertime cold, wet bias in the desert southwest;

**•** Surface temperature and humidity performance now within benchmarks for all WRAP subdomains except desert southwes t for temperature;

**•** More accurate representation of the diurnal temperature cycle in the desert southwest;

**•** Improvements in the index of agreement for temperature, humidity and wind speed for all WRAP subdomains;

**•** A more realistic precipitation pattern over t he western U.S.;

**•** Better model performance in the eastern U.S.; and

**•** An improvement in performance throughout the year, making it unnecessary to select different physics schemes for different seasons.

A full discussion of MM5 sensitivity testing and optimiz ation can be found in Kemball

- Cook et al., ( 2004 ) 28

.

D. Model Performance

The model performance of MM5 in the 36

- km simulations is described by the final RMC modeling report (Kemball

- Cook et al., 2005) 26

. The goal of the evaluation was to determine

whether the meteorological fields are sufficiently accurate to characterize properly the transport, chemistry, and removal processes in CMAQ. If errors in the meteorological fields are too large, the ability of the air quality model to replicate regional pollutant levels over the entire base year

will be reduced and the predicted impacts from future year growth and controls will have greater uncertainty . To provide a reasonable meteorological characterization to the photochemical/visibility model, MM5 must represen t with some fidelity the:

**•** Large

- scale weather patterns (i.e., synoptic patterns depicted in the 850

- 300 mb height fields), as these are key forcings for mesoscale circulations;

**•** Mesoscale and regional wind, temperature, PBL height, humidity, and cloud/prec ipitation
patterns;

**•** Mesoscale circulations such as sea breezes and mountain/drainage circulations;

**•** Diurnal cycles in PBL depth, temperature, and humidity.

For visibility applications, the moisture and condensate fields are particularly important as they

s ignificantly impact PM chemical formation and removal by wet and dry deposition . In addition,

28 Kemball

- Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2004. “MM5 Sensitivity Simulations to Identify a More Optimal MM5 Configuration Sensitivity T esting.” Revised Report. ENVIRON International Corporation and UC Riverside. Available at: http://pah.cert.ucr.edu/aqm/308/mm5.shtml28 cloud and precipitation fields are a good measure of the integrated performance of the model

since these are model

- derived quantities and not nudged to observatio ns. Because of the model’s

coarse resolution of 36

- km, the model cannot be expected to faithfully simulate the pattern or variability of the convective precipitation, but should reproduce the synoptic precipitation and cloud patterns.

The RMC performed a comprehensive model performance evaluation for the 2002 MM5 simulation. This included a operational evaluation and a diagnostic evaluation using sensitivity simulation to optimize model performance for the western US. An operational evaluation entails

an assessment of the model's ability to correctly estimate surface and boundary layer wind, temperature, and moisture largely independent of whether the actual process descriptions in the model are accurate. The operational evaluation essentially tests whethe r the predicted meteorological fields are reasonable, consistent, and agree adequately with available observations in time and space.

The basis for the RMC operational performance assessment entailed a comparison of the predicted meteorological fields to available surface and aloft data that are collected, analyzed, and disseminated by the National Weather Service. It was carried out both graphically and statistically to evaluate model performance for winds, temperatures, humidity, and the placement, inte nsity, and evolution of key weather phenomena. The MM5 results were compared to a specific set of statistics that have been identified for use in establishing benchmarks for acceptable MM5 model performance (Emery et al., 2001) 29

.

The RMC concluded, based on the results of the performance evaluation, that the final 36 km WRAP MM5 simulations exhibit reasonably good performance that is within the bounds of other meteorological databases used for prior air quality modeling efforts. It was therefore deemed

r easonable to proceed with its use as inputs for visibility modeling.

Review Comment: The MM5 meteorological model used by WRAP was state

- of

- the

- science at the time the modeling was conducted and the performance of the model was adequate for the

purposes for which it was used and on par with other studies at the time (Emery et al., 2001).

E. MM5 Processing and Application

Several preprocessing steps are necessary to prepare input data for an MM5 simulation. The MM5 modeling system provides all of the tool s necessary to prepare topographic, vegetative, initial condition, boundary condition, and FDDA nudging input files.

Global topographic data at 10

- minute (latitude/longitude) resolution were used to define terrain

elevations on the 36

- km grid. Land use di stribution on the MM5 domains was defined from the

24

- category USGS vegetation data with a resolution of 10 minutes.

29 Emery, C.A. and E. Tai. 2001. “Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes.” Prepared for the Texas Natural Resource Conservation Commission, by ENVIRON International Corporation.29 The underlying objective analyses used for the 2002 WRAP simulation were taken from the Eta Data Assimilation System (EDAS). EDAS is avail able from NCAR, and contains 3

- hourly objective analysis initialization and forecast fields from the National Center for Environmental Prediction’s (NCEP) ETA model, which is the current short

- term national operational forecasting platform. The EDAS analys es are provided on a standardized continental

- scale pressure

- level Lambert grid with ~40 km horizontal grid point spacing. The EDAS analyses are developed from a wide variety of observational sources, including standard surface and upper air measurements, profiler networks, radar

- and satellite

- derived measurements, and ship and aircraft reports. The wide array of data sources, coupled with the high time and spatial resolution provided by EDAS, result in an analysis product that far exceeds the level of det ail found in traditional global

- scale analyses.

The EDAS analyses were processed for use by MM5 as initial/boundary conditions, and for analysis nudging in the FDDA package. In addition, the raw EDAS was enhanced for non

standard pressure levels. Sea sur face temperatures (SSTs) were approximated by ETA skin temperatures, and the SSTs were allowed to vary over the course of the simulation. In addition, the NCEP ADP Global Surface Observations and the NCEP ADP Upper Air Observations were used to develop t he 2

- D FDDA fields for use in MM5.

The annual simulation was made in sequential 5

- day run segments, each with an initial spinup period of 12 hours that overlapped the last 12 hours of the preceding run. The model was spun up for the final two weeks of D ecember 2001 to allow for photochemical/visibility applications with start dates at the beginning of January 2002. 30



### 5. Visibility Modeling

The WRAP has created a document titled Air Quality Modeling 31 that describes the visibility modeling tools that they us ed to develop technical products in support of SIP development.

Excerpts of WRAP’s Air Quality Modeling document referenced here were drafted prior to the development of the final base case planning emissions inventory called ‘Plan02d’, and prior to

the development of the final 2018 future

- year e missions inventory called ‘PRP 18b’. However, the excerpts of Air Quality Modeling below remain applicable to those later emissions scenarios, and where appropriate, EPA has added explanatory as well as review co mments.

Air Quality Models

The WRAP RMC utilized two regulatory air quality modeling systems to conduct all regional haze modeling. A brief discussion of each of these models is provided below.

Community Multi

- Scale Air Quality Model

30 A list of the run segments and their date/time durations is provided at http://pah.cert.ucr.edu/aqm/308/2002met/mm5/2002_MM5_dateskey_UCR.xls

31 http://vista.cira.colostate.edu/d ocs/wrap/Modeling/AirQualityModeling.doc30 EPA initially developed the Community Multi

- Scale Air Quality (CMAQ) modeling system in the late 1990s. The model source code and supporting data can be downloaded from the Community Modeling and Analysis System (CMAS) Center 32 , which is funded by EPA to distribute and p rovide limited support for CMAQ users. CMAQ was designed as a “one atmosphere” modeling system to encompass modeling of multiple pollutants and issues, including ozone, PM, visibility, and air toxics. This is in contrast to many earlier air quality

models that focused on single

- pollutant issues (e.g., ozone modeling by the Urban Airshed Model). CMAQ is an Eulerian model

— that is, it is a grid

- based model in which the frame of reference is a fixed, three

- dimensional (3

- D) grid with uniformly sized horizontal grid cells and variable vertical layer thicknesses. The number and size of grid cells and the number and thicknesses of layers are defined by the user, based in part on the size of the modeling domain to be used for each modeling project. The key science p rocesses included in CMAQ are emissions, advection and dispersion, photochemical transformation, aerosol thermodynamics and phase transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a variety

of choices in the numerical al gorithms for treating many of these processes, and it is designed so that new algorithms can be included in the model. CMAQ offers a choice of three photochemical mechanisms for solving gas

- phase chemistry: the Regional Acid Deposition Mechanism version

2 (RADM2), a fixed coefficient version of the SAPRC90 mechanism, and the Carbon Bond IV mechanism (CB

- IV).

Comprehensive Air Quality Model with Extensions

The Comprehensive Air Quality Model with extensions (CAMx) model was initially developed by ENVIRON in the late 1990s as a nested

- grid, gas

- phase, Eulerian photochemical grid model. ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many similarities between the CMAQ and CAMx systems, there are also some significant diff erences in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition.

Explanatory Comment: WRAP used CMAQ modeling for model performance evaluations with the Base02b emissions inventory, and the n used CMAQ with Plan02d and P RP 18b emissions inventories to generate the relative response factors (RRF) needed to project 2018 future

- year visibility. WRAP used CAMx modeling for model performance evaluations with the Base02b emissions inventory, and then used CAMx with its Particul ate Source Apportionment Technology (PSAT) tool to provide source apportionment of nitrate and sulfate aerosol with both the

Plan02c and Base18b emissions inventories.

Review Comment: States and Tribes are likely to rely heavily on the CAMx PSAT result s to quantify the sources of nitrate and sulfate impacting their Class I Areas and, therefore, to shape their approach to emissions controls. However, WRAP has provided visibility projections for 2018 using the CMAQ model, not CAMx. WRAP compared the mod el performance of CMAQ and CAMx using the Base02b emissions inventory and found them comparable, but not identical. While WRAP’s use of different models for different products in this case is not considered significant, it is advised that in the future th ere be better alignment in the choice of models so that there is no perceived inconsistency.

32 http://www.cmascenter.org/31 Model Versions

Both EPA and ENVIRON periodically update and revise their models as new science or other improvements to the models are developed. WRAP elected to operate CMAQ v4.5 using the MM5 data processed using MCIP v2.3 and the AE3 aerosol module. The version used for the comparison of CMAQ and CAMx was CAMx v4.3.

Review Comment: The versions of CMAQ and CAMx used by WRAP in its visibility modeling

wer e the state

- of

- the

- science at the time they were implemented

.

Major differences between the two models that still exist are in the basic model code, in the treatment of horizontal diffusion SOA formation mechanisms, and in grid nesting. The publicly

rel eased version of CAMx supports ozone and PM source apportionment through its Ozone and PM Source Apportionment Technology (OSAT/PSAT) probing tools.

Model Simulations

In support of the WRAP Regional Haze air quality modeling efforts, the RMC developed a ir quality modeling inputs including annual meteorology and emissions inventories for a 2002 actual emissions base case, a planning case to represent the 2000

- 04 regional haze baseline period using averages for key emissions categories, and a 2018 base cas e of projected emissions. All emission inventories were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. Each of these inventories has undergone a number of revisions throughout the development process to arrive at the f inal versions used in CMAQ and CAMx air quality modeling. The development of each of these emission scenarios is as follows:

**•** The 2002 base case emissions scenario, referred to as “2002 Base Case” or “Base02”. The purpose of the Base02 inventory is to represent the actual conditions in calendar year 2002 with respect to ambient air quality and the associated sources of criteria and particulate matter air pollutants. The Base02 emissions inventories are used to evaluate the air quality

model and associa ted databases and to demonstrate acceptable model performance with respect to replicating observed particulate matter air quality.

Explanatory Comment: The final base case emissi ons inventory used was called ‘B ase02b’. For a review of this emissions in ventory see the emissions inventory development section of this

document.

**•** The 2000

- 04 baseline period planning case emissions scenario is referred to as “Plan02”. The purpose of the Plan02 inventory is to represent baseline emission patterns based on av erage, or “typical”, conditions. This inventory provides a basis for comparison with the future year 2018 projected emissions, as well as to gauge reasonable progress with respect to future year visibility.

Explanatory Comment: The final baseline plan ning case used was called ‘P lan02d’. For a review of this emissions inventory see the emissions inventory development section of this

document.32 **•** The 2018 future

- year base case emissions scenario, referred to as “2018 Base Case” or

“Base18”. These emissio ns are used to represent conditions in future year 2018 with respect to sources of criteria and particulate matter air pollutants, taking into consideration growth and controls. Modeling results based on this emission inventory are used to define the futur e year ambient air quality and visibility metrics.

Explanatory Comment: The final future

- ye ar scenario used was called ‘PRP 18b’. For a review of this emissions inventory see the emissions inventory development section of this document.

Data Sources

The CMAQ model requires inputs of three

- dimensional gridded wind, temperature, humidity, cloud/precipitation, and boundary layer parameters. All meteorological data used for the WRAP air quality modeling efforts are derived from MM5 model simulations.

Review Comment: Meteorological modeling is reviewed elsewhere in this document.

Review Comment: WRAP conducted a sensitivity simulations comparing model performance at 36 and 12 km horizontal resolution and found no appreciable performance improvement in the model outputs at 12 km resolution over those at 36 km resolution. At the time, given resource constraints and the lack of improved performance, WRAP decided to conduct all emissions, meteorological, and visibility modeling at 36 km horizontal reso lution. WRAP’s choice of 36 km horizontal resolution was appropriate given the computer and resource limitations and the lack of improved performance at 12 km resolution.

Emission inventories for all WRAP air quality simulations were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system.

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For

continental

- scale modeling using the RPO Unified 36

- km domain, the ICs can affect model

res ults for as many as 15 days, although the effect typically becomes very small after about 7 days. A model spin

- up period is included in each simulation to eliminate any effects from the ICs. For the WRAP modeling, the annual simulation is divided into four quarters, and included a

15

- day spin

- up period for the quarters beginning in April, July, and October. For the quarter beginning in January 2002, a spin

- up period covering December 16

- 31, 2001, using meteorology and emissions data developed for CENRAP wer e used.

Review Comment: The 15 day spin

- up period employed by WRAP was sufficient given the size of the modeling domain.

Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral

boundaries of the model domain. BCs determine the amounts of gas and PM species that are transported into the model domain when winds flow is into the domain. Boundary conditions have a much larger effect on model simulations than do ICs. For some areas in the WRAP region

and for clean cond itions, the BCs can be a substantial contributor to visibility impairment. For33 this study BC data generated in an annual simulati on of the global

- scale GEOS

- CHEM model that was completed by Jacob et al. ( http://acmg.seas.harvard.edu/geos/ ) for calendar ye ar 2002 were applied.

Review Comment: Boundary conditions employed by WRAP were state

- of

- the

- science at the time they were implemented.

The CMAQ model options and configuration used for the WRAP 36

- km model simulatio ns are described in Tonnesen et al ., 2006 33

.

2002 Base Case Modeling

The purpose of the 2002 Base Case modeling efforts was to evaluate air quality/visibility modeling systems for a historical episode

— in this case, for calendar year 2002

— to demonstrate the suitability of the modeling syst ems for subsequent planning, sensitivity, and emissions control strategy modeling. Model performance evaluation is performed by comparing output from model simulations with ambient air quality data for the same time period.

Model Performance Evaluation

The objective of a model performance evaluation (MPE) is to compare model

- simulated concentrations with observed data to determine whether the model’s performance is sufficiently accurate to justify using the model for simulating future conditions. There a re a number of challenges in completing an annual MPE for regional haze. The model must be compared to ambient data from several different monitoring networks for both PM and gaseous species, for an annual time period, and for a large number of sites. The model must be evaluated for both the worst visibility conditions and for very clean conditions. Finally, final guidance on how to perform an MPE for fi ne

- particulate models was not available from EPA when WRAP completed its modeling . Therefore, the RMC exp erimented with many different approaches for showing model performance results. The plot types that were found to be the most useful are the following:

**•** Time

- series plots comparing the measured and model

- predicted species concentrations

**•** Scatter plots showi ng model predictions on the y

- axis and ambient data on the x

- axis

**•** Spatial analysis plots with ambient data overlaid on model predictions

**•** Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) performance metrics

**•** “Bugle plots” sh owing how model performance varies as a function of the PM species

concentration

**•** Stacked

- bar plots of contributions to light extinction for the average of the best

- 20% visibility days or the worst

- 20% visibility days at each site; the higher the light exti nction, the lower the visibility

33 http://pah.cert.ucr.edu/aqm/308/reports/final/ 2002_MPE_report_main_body_FINAL.pdf34 Explanatory Comment: The following plots depict summary model performance for WRAP 200 2 Base Case modeling using the B ase02b emissions inventory and were downloaded from the RMC web site. Below are three sets of model bi as and model error plots. Each set of plots compares the measured chemically speciated aerosol data from a monitoring network with the corresponding model output. The monitoring networks used for comparison are IMPROVE, CASTNET, and STN, and are treated separately because each monitoring network has different goals, siting criteria, and data collection protocols. The model performance plots depicted here are “bugle plots”, and depict model performance (symbols) and model performance standards (curves) on the y axis relative to measured concentration on the x axis. Model performance standards are of greater latitude at lower concentrations because of the higher relative uncertainties in the data at lower concentrations. There are twelve symbols for each chemical species, which represents the monthly model performance for 2002. Model performance at IMPROVE monitors is of highest importance, because these monitors are sited to be representative of the visibility conditions impacting each Class I Area. The CASTNET monitoring network is more sparse than the IMPROVE network, but is also mostly sited at Class

I Areas and as such, model performance at CASTNET sites should also be considered important. The STN monitoring network is an urban network, and model p erformance relative to this network should be given less importance.35 Figure 5 .1. WRAP model performance (factional bias and error) of the Base02b modeling scenario for chemically speciated aerosol data from the IMPROVE monitoring network. The 12 symb ols for each chemical species represent monthly average model performance for the year 2002, averaging all monitors in the WRAP region. Solid lines represent WRAP modeling goals and criteria. For chemical species acronym explanations, see text below.36 Figure 5 .2. WRAP model performance (factional bias and error) of the Base02b modeling scenario for chemically speciated aerosol data from the CASTNET monitoring network. The 12 symbols for each chemical species represent monthly average model perform ance for the year 2002, averaging all monitors in the WRAP region. Solid lines represent WRAP modeling goals and criteria. For chemical species acronym explanations, see text below.37 Figure 5 .3. WRAP model performance (factional bias and error) of the Base02b modeling scenario for chemically speciated aerosol data from the STN monitoring network. The 12 symbols for each chemical species represent monthly average model performance for the year 2002, averaging all monitors in the WRAP region. Solid lin es represent WRAP modeling goals and criteria. For chemical species acronym explanations, see text below

.38 Review Comment: The model performance goals and criteria used by WRAP were appropriate at the time the modeling was conducted.

Review Comment: For IMPROVE and CASTNET monitoring sites, model performance was adequate based on stated criteria except for course particulate matter (CM). WRAP addressed this model performance issue by setting all CM relative response factors (RRF) to a value of 1

wh en projecting course matter visibility impacts to the future year 2018, regardless of the future year modeling results. This is considered acceptable for most sites because CM that is both anthropogenic and controllable is not believed to be of major impo rtance for visibility impairment at most Class I Areas. However, where anthropogenic course PM is suspected of being a significant contributor, SIP developers should not rely on WRAP CMAQ model projections and should provide alternate technical analyses.

Review Comment: For STN monitoring sites, WRAP modeling showed significant underpredictions for all species and was beyond the criteria for nitrate and organic carbon. However, these model underpredictions are expected given that WRAP’s model resoluti on was 36 km and urban areas are known to have significantly inhomogeneous PM concentrations over a scale of 36 km. Typically, all model emissions are immediately diluted into the grid box they are assigned to. Hence, monitors that experience emissions a t time scales that don’t allow for the dilution that is assumed in the model are likely to show model underpredictions.

Review Comment: The above model performance summary includes all sites within the WRAP. However, a model performance summary over such a diverse geographic area may mask model performance issues occurring in smaller geographic sub

- regions. WRAP performed some limited experiments analyzing model performance on a sub

- regional basis, however, WRAP technical staff never arrived at an ag reed upon methodology to define the sub

- regions. There were many factors to consider when defining sub

- regions for model performance, some of which were conflicting. Additionally, including sub

- regional model performance analysis was

considered resource prohibitive at the time. In future Regional Haze modeling and analyses, it is recommended that more work be done to define coherent sub

- regions for model performance analysis so that this issue is addressed. Special attention should be paid to meteorolog ical model performance in the desert southwest, as this was a recognized problem in the WRAP modeling. If possible, also consider including model performance plots similar to the bugle plots

above on a site

- by

- site basis.

2002 Planning Scenario

Inpu t data used for the 2002 Planning model simulations consisted of the same meteorology as

for the 2002 Base Case and the Plan02 emission inventories described under the Emissions Modeling section of the TSS.

The setup of the CMAQ model (including science options, run scripts, simulation periods, and ancillary data) for the Plan02 cases was identical to that used in the Base02 modeling, as described in the 2002 MPE report (Tonnesen et al., 2006).

Comparison With Base02 Simulations39 For each of the Plan02 emissions datasets, annual visibility modeling was performed using the CMAQ model. This was a key aspect of the QA procedure, since errors in the emissions inventories that might not be apparent during the emissions QA steps might be more readily

detected in the results from the CMAQ modeling.

Explanatory Comment: WRAP RMC compared CMAQ output using Plan02d emissions with CMAQ output using Base02b emissions by plotting the differences between the two air quality model runs for daily, monthly, and annual averages for each chemical species predicted. For a description and comparison of the Base02b and Plan02d emissions inventories, see section 3

above.

Note that these plots are not useful for visibility planning purposes, but are being provided to

show the magnitudes of changes when moving from the 2002 Base Case to the 2002 Planning

Case

— in other words, from the actual emissions for the year 2002 to the “typical

- year” emissions created for the final Plan02 scenario. The primary analysis “product” from the Plan02 CMAQ modeling is the use of its output in combination with the CMAQ output from the 2018 modeling to develop the visibility progress calculations and glide path plots, described

below.

2018 Model Simulations

The 2018 future

- year base case scen ario is referred to as “2018 Base Case” or “Base18”. The purpose of the Base18 scenario is to simulate the air quality representative of conditions in future year 2018 with respect to sources of criteria and particulate matter air pollutants, taking into consideration growth and controls. Modeling results based on this emission inventory are used to define the future year ambient air quality and visibility metrics.

Input data used for the 2018 Base Case model simulations consisted of the same meteorology as for the 2002 Base Case and the Base18 emission inventories described under the Emissions Modeling section of the TSS.

The setup of the CMAQ model (including science options, run scripts, simulation periods, and ancillary data) for the Base18 cases wa s identical to that used in the Base02 modeling, as described in the 2002 MPE report (Tonnesen et al., 2006).

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 typical

- year visibility modeling results, as d iscussed below. Some improvements in visibility by 2018 are expected because of reductions in emissions due to currently planned regulations and technology improvements.

Visibility Projections

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as (1) visibility improvement toward natural conditions for the 20% of days that have the worst40 visibility (termed “20% worst,” or W20% , visibility days) and (2) no worsening in visibility for the 20% of days that have the best visibility (“20% best,” or B20%, visibility days). One component of the states’ demonstration to EPA that they are making reasonable progress toward this 2064 goal is the comparison of modeled visibility projections for the first milestone year of 2018 with what is termed a uniform rate of progress (URP) goal. As explained in detail below, the 2018 URP goal is obtained by constructing a “linear glide path” (in deciv iews) that has at one end the observed visibility conditions during the mandated five

- year (2000

- 2004) baseline period and at the other end natural visibility conditions in 2064; the visibility value that occurs on the glide path at year 2018 is the URP go al.

Explanatory Comment: WRAP has made 2018 visibility projecti ons using Plan02d and PRP 18b CMAQ 36

- km modeling results

[F]ollowing EPA guidance that recommends applying the modeling results in a relative sense to project future

- year visibility conditi ons (U.S. EPA, 2001 34 , 2003a 35 , 2006 36 ). Projections are made using relative response factors (RRFs), which are defined as the ratio of the future

- year modeling results to the current

- year modeling results. The calculated RRFs are applied to the

baseline obse rved visibility conditions to project future

- year observed visibility. These projections can then be used to assess the effectiveness of the simulated emission control strategies that were included in the future

- year modeling. The major features of EPA’s

r ecommended visibility projections are as follows (U.S. EPA, 2003a,b 37 , 2006):

**•** Monitoring data should be used to define current air quality.

**•** Monitored concentrations of PM10 are divided into six major components; the first five are

assumed to be PM2.5 and t he sixth is PM2.5

- 10.

o SO4 (sulfate)

o NO3 (particulate nitrate)

o OC (organic carbon)

o EC (elemental carbon)

o OF (other fine particulate or soil)

o CM (coarse matter).

**•** Models are used in a relative sense to develop RRFs between future and current predicted

concent rations of each component.

**•** Component

- specific RRFs are multiplied by current monitored values to estimate future component concentrations.

**•** Estimates of future component concentrations are consolidated to provide an estimate of future air quality.

34 U.S. EPA. 2001. “Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 and Regional Haze”, Draft Report, U.S. Environmental Protection Agency, Research Triangle Park, NC.

35 U.S. EPA. 2003a. “Guidance fo r Estimating Natural Visibility Conditions under the Regional Haze Rule.” EPA

454/B

- 03

- 005. September 2003

36 U.S. EPA. 2006 . Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, P M 2.5 , and Regional H aze

– Draft 3.2. U.S. Environmental Protection Agency, Office of Air Quality and Planning Standards, Research Triangle Park, North Carolina. September. ( http://www.epa.gov/scr am001/guidance/guide/draft_final

- pm

- O3

- RH.pdf ).

37 U.S. EPA. 2003b. “Guidance for Tracking Progress under the Regional Haze Rule.” U.S. EPA, EPA

- 454/B

- 03

- 004. September 2003.41 **•** Future es timated air quality is compared with the goal for regional haze to see whether the simulated control strategy would result in the goal being met.

**•** It is acceptable to assume that all measured sulfate is in the form of ammonium sulfate [(NH4)2SO4] and all pa rticulate nitrate is in the form of ammonium nitrate [NH4NO3].

To facilitate tracking the progress toward visibility goals, two important visibility parameters are required for each Class I area:

**•** Baseline Conditions: “Baseline Conditions” represent visib ility for the B20% and W20% days for the initial five

- year baseline period of the regional haze program. Baseline Conditions are calculated using monitoring data collected during the 2000

- 2004 five

- year period and are the starting point in 2004 for the uni form rate of progress (URP) glide path to Natural Conditions in 2064 (U.S. EPA, 2003a).

**•** Natural Conditions: “Natural Conditions,” the RHR goal for 2064 for the f ederally mandated Class I areas, represent estimates of natural visibility conditions for the B 20% and W20% days at a given Class I area.

Mapping Model Results to IMPROVE Measurements

As noted above, future

- year visibility at Class I areas is projected by using modeling results in a relative sense to scale current observed visibility for the B20% and W20% visibility days. This scaling is done using RRFs, the ratios of future

- year modeling results to current

- year results. Each of the six components of light extinction in the IMPROVE reconstructed mass extinction equation is scaled separately. Becaus e the modeled species do not exactly match up with the IMPROVE measured PM species, assumptions must be made to map the modeled PM species to the IMPROVE measured species for the purpose of projecting visibility improvements.

Projecting Visibility Change s Using Modeling Results

RRFs calculated from modeling results can be used to project future

- year visibility. For the current modeling efforts, RRFs are the ratio of the 2018 modeling results to the 2002 modeling results, and are specific to each Class I area and each PM species. RRFs are applied to the Baseline Condition observed PM species levels to project future

- year PM levels, which are then used with the IMPROVE extinction equation to assess visibility. The following six steps are used to project fut ure

- year visibility for the B20% and W20% visibility days (the discussion below is for W20% days but also applies to B20% days):

1. For each Class I area and each monitored day, daily visibility is ranked using IMPROVE data and IMPROVE extinction equation fo r each year from the five

- year baseline period (2000

- 2004) to identify the W20% visibility days for each year.

2. Use an air quality model to simulate a base

- year period (ideally 2000

- 2004, but in reality just 2002) and a future year (e.g., 2018), then apply the resulting information to develop Class

- I

area

- specific RRFs for each of the six components of light extinction in the IMPROVE aerosol extinction equation.42 3. Multiply the RRFs by the measured 24

- h PM data for each day from the W20% days for each year from the five

- year baseline period to obtain projected future

- year (2018) 24

- h PM concentrations for the W20% days.

4. Compute the future

- year daily extinction using the IMPROVE aerosol extinction equation and the projected PM concentrations for each of the W20% days in the five

- year baseline from Step 3.

5. For each of the W20% days within each year of the five

- year baseline, convert the future

year daily extinction to units of deciview and average the daily deciview values within each of the five years separately t o obtain five years of average deciview visibility for the W20% days.

6. Average the five years of average deciview visibility to obtain the future

- year visibility Haze Index estimate that is compared with the 2018 progress goal.

Review Comment: The six ste ps listed above are an appropriate application of national EPA modeling guidance for regional haze at the time the modeling was performed.

Glide Path to Natural Conditions

A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 is assumed, and the value on the glide path at 2018 is the presumptive URP visibility target that the modeled 2018 projections are compared against to judge progress.

Visibility Projection Results

For all of the WRAP Class I areas, the RMC performed prel iminary 2018 visibility projections and compared them to the 2018 URP goals.

Explanatory Comment: WRAP performed visibility projections to 2018 and compared them to the 2018 URP goals using the Plan02d scenario for the base year period and the PRP 18b

sce nario for 2018. These emissions scenarios were modeled with CMAQ and the resulting RRFs were applied to baseline monitoring data using the new IMPROVE equation. For a review of the 6 steps listed above to project future year visibility, see the review co mments in Chapter 5

.

Review Comment: A requirement for an approvable Regional Haze SIP is the establishment of reasonable progress goals that provide for an improvement in visibility for the most impaired days over the period of the implementation plan an d ensure no degradation in visibility for the least impaired days over the same period. Region 6's preliminary review indicates that the visibility modeling performed by the WRAP results in modeled degradation of visibility

conditions in 2018 at two Class I Areas in New Mexico on the 20% best days (Gila Wilderness and Carlsbad Caverns) and at Gila Wilderness on the 20% worst days. Initial examination into the cause of this modeled degradation reveal possible inconsistencies and/or errors between

emission inventory scenarios that may be large enough to significantly affect model results at

these Class I areas. A draft document containing the preliminary analysis can be obtained from Erik Snyder or Michael Feldman of EPA Region 6 . This document details a p reliminary review of some of the WRAP emission inventories and modeling results for New Mexico. Similar issues may affect model results at other Class I areas within the WRAP region. We note that the43 WRAP's modeling for 2018 also indicated that the foll owing Class I areas (may not be a complete list) have modeled visibility degradation on the 20% worst days.

MONT: Bob Marshall W, MT: Mission Mountains W, MT: Scapegoat W, MT NOCA: Glacier Peak W, WA: North Cascades NP, WA CHIR: Chiricahua NM NM, AZ: Chi ricahua W W, AZ: Galiuro W, AZ



### 6. Source Apportionment Modeling

The WRAP performed five types of attribution analyses to visibility

- related data in an effort to better understand source region impacts of emissions on visibility. Discussed in the follow ing subsections, those analyses include:

**•** PM Source Apportionment Technology (PSAT) analysis

**•** Weighted Emissions Potential analysis

**•** Organic Aerosol Tracer analysis

**•** Positive Matrix Factorization analysis

**•** Causes of Dust analysis

A. PM Source Apportionment T echnology (PSAT) Analysis

Visibility impairment in Class I areas is the result of local air pollution as well as transport of regional pollution across long distances. The relative contributions to visibility impairment from each source region and catego ry is needed to develop effective control strategies to improve visibility. WRAP used CAMx Version 4.30 with its Particulate Source Apportionment Technology (PSAT) tool to provide source apportionment by geographic regions and major

source category. CAMx was run with similar options and inputs as the C M AQ modeling with

both the 2002 baseline and 2018 future case emission inventories. PSAT uses reactive tracers that operate in parallel to the CAMx host model using the same emissions, transport, chemical

t ransformation and deposition rates as the host model to account for the contributions of user specified source regions and categories to PM concentrations throughout the modeling domain. Details on the formulation of the CAMx PSAT source apportionment can be found in the CAMx user’s guidance 38 . The goals of the PSAT assessment are to evaluate the contributions of different geographic regions and source categories to visibility impairment at Class I areas in 2002 and the projected 2018 case in order to ident ify those regions and source categories that, if controlled, would produce the greatest improvements in visibility. Further information regarding

the PSAT analysis technique can be found in the TSS document PM Source Apportionment Technology (PSAT) Analys is 39

The WRAP defined 18 geographical source regions consisting of the individual WRAP states, Nevada, the CENRAP states, Canada, Mexico, Pacific off

- shore and the remainder of the eastern U.S. including the Gulf of Mexico and the Atlantic (Table 6.1, Figu re 6.1). Six source categories

38 ENVIRON. 2006. “User’s Guide

– Comprehensive Air

- quality Model with extensions, Version 4.30.” ENVIRON International Corporation, Novato, California . (a vailable at http://www.camx.com ).

39 http://vista.cira.colostate.edu/docs/wrap/attribution/PSATMethods.doc44 (point, mobile, area, anthropogenic wildfires (WRAP), natural or non

- anthropogenic sources (WRAP), and sources outside of the modeling domain) were tracked separately. The PSAT modeling focuses on sulfate and nitrate contr ibution only and takes into account chemistry and deposition. Contributions for the 20% worst and 20% best days at each WRAP and nearby Class I area were extracted from the PSAT results. A PSAT Visualization Tool was developed that can be used by States, Tribes and others to generate displays of the contributions of source regions and categories to visibility impairment for the average of the worst 20 percent and best 20 percent days at each WRAP and nearby Class I areas 40

.

PSAT results rely on the model’s value for each of the individual modeled species in calculating the contribution. Therefore, the PSAT results are directly impacted by model performance issues. In reviewing PSAT results, an evaluation of model performance for that species for the speci fic Class I monitor that is being evaluated should be taken into consideration. EPA does not have any specific guidance on how to conduct source apportionment modeling. The WRAP

used state

- of

- the

- science source apportionment tools within a widely used p hotochemical model. EPA has reviewed the PSAT tools and techniques that were used in the PSAT analysis and considers the analysis acceptable.

For sites where the state establishes a reasonable progress goal that does not meet the Uniform Rate of Progres s to attain natural conditions by 2064, the s tate must demonstrate that the rate of progress for the implementation plan to attain natural conditions by 2064 is not reasonable and that the progress goal adopted by the State is reasonable. PSAT results alo ng with other source apportionment techniques and analysis of emission inventories is required to determine the causes of future visibility impairment and support the conclusion that the proposed RPG is

reasonable.

Table 6.1. WRAP CAMx/PSAT source regions

. Source Region ID Source Region Description Source Region ID Source Region Description

1 Arizona (AZ) 10 South Dakota (SD)

2 California (CA) 11 Utah (UT)

3 Colorado (CO) 12 Washington (WA)

4 Idaho (ID) 13 Wyoming (WY)

5 Montana (MT) 14 Pacific off

- shore & Sea of Cortez (OF)

6 Nevada (NV) 15 CENRAP states (CE)

7 New Mexico (NM) 16 Eastern U.S., Gulf of Mexico, & Atlantic Ocean (EA)

8 North Dakota (ND) 17 Mexico (MX)

9 Oregon (OR) 18 Canada (CN)

40 available at http://vista.cira.colostate.edu/TSS/Results/SA.aspx45 -2736 -2412 -2088 -1764 -1440 -1116 -792 -468 -144 180 504 828 1152 1476 1800 2124 2448 -2088 -1872 -1656 -1440 -1224 -1008 -792 -576 -360 -144

72 288 504 720 936 1152 1368 1584 1800

2 6 9 12

4 5 8

13

11 3

1 7

15 15

16

16 16 18 18

14

17 14

10

Figure 6.1. WRAP CAMx/PSAT source region map. T able 6 defines the source region IDs.

Review Comment: The CAMx model selection and performance are reviewed elsewhere in this

document.

Review Comment: The CAMx PSAT analysis has been tested and evaluated against other apportionment techniques 41

, 42

.

Review Comment: PSAT results from CAMx utilized the Plan02c and Base 18b emission inventories, while CMAQ modeling to derive RRFs and the WEP analyses are pe rformed with

the Plan02d and PRP 18b emission inventories. There are a number of differences between these emission inventories (e.g. Plan02c does not include any biogenic emissions of NO

x or VOC from

Mexico ; PRP 18b includes projected emissions for Mexico) that impact visibility projections and must be considered when interpreting results.

B. Weighte d Emissions Potential

The Weighted Emissions Potential (WEP) analysis was developed as a screening tool for states to decide which source regions have the potential to contribute to haze formation at specific Class I areas, based on both the Baseline and 2018 emissions inventories. Unlike the PSAT analysis described above, this method does not account for chemistry and removal processes.

41 Morris, R.E., G.Y., C.E., G.W., B.K. 2005. “Recent Advances in One

- Atmospheric Modeling Using the Comprehensive Air

- quality Model with Extensions.” Presented at t he 98th Annual Air and Waste Management Conference, Minneapolis, MN. June.

42 Yarwood, G., R.E. Morris, G. Wilson. 2004. “Particulate Matter Source Apportionment Technology (PSAT) in

the CAMx Photochemical Grid Model." Presented at the ITM 27th NATO Confer ence

- Banff Centre, Canada,

October.46 Instead, the WEP analysis relies on an integration of gridded emissions data, meteorological back trajectory residence time data, a one

- over

- distance factor to approximate deposition, and a normalization of the final results. Residence time over an area is indicative of general flow patterns, but does not necessarily imply the area contributed significantly to haze at a given receptor. Therefore, users are cautioned to view the WEP analysis as one piece of a larger, more comprehensive weight of evidence analysis. Further information regarding the WEP analysis technique can be found in the TSS document Weighted Emissions Potential Analysis 43

.

The emissions data used were the annual, 36km grid SMOKE

- processed, model

- ready emissions inventories provided by the WRAP Regional Modeling Center (RMC) 44 . The analysis was performed for nine (9) pollutants (maps were generated for all but the last three): sulfur oxides, nitrogen oxides, organic carbon, elemental carbon, fine particulate matter, coarse particulate matter, ammonia, volatile organic carbon, carbon monoxide. The following source categories for each pollutant were ident ified and preserved through the analysis: Biogenic, Natural fire, Point, Area, WRAP oil and gas, Off

- shore, On

- road mobile, Off

- road mobile, Road dust, Fugitive dust, Windblown dust, Anthropogenic fires

The back trajectory residence times were provided by the WRAP Causes of Haze Assessment (COHA) 45 . The COHA project used NOAA’s HYSPLIT model to generate eight (8) back trajectories daily for each WRAP Class I area for the entire five

- year baseline period (2000

- 04). The major model parameters selected for this analysis are presented in Table 6.2 . From these individual trajectories, residence time fields were generated for one

- degree latitude by one

degree longitude grid cells. Residence time analysis computes the amount of time (e.g., number of hours) or percent of time an air parcel is in a horizontal grid cell. Plotted on a map, residence time is shown as percent of total hours in each grid cell across the domain, thus allowing an interpretation of general air flow patterns for a given Class I area. T he residence time fields for

the 20% worst and best IMPROVE

- monitored extinction days were selected for the WEP analysis to highlight the potential emissions sources during those specific periods.

Table 6.2. Back Trajectory Model Parameters Selected for WEP Analysis

Model Parameter Value Trajectory duration 192 hours (8 days) backward in time Top of model domain 14,000 meters Vertical motion option used model data Receptor height 500 meters Meteorological Field EDAS and FNL (location dependent)

Rev iew comment: The receptor height of 500 meters may be more appropriate for secondary particulate matter than for course particulate matter.

The WEP analysis consisted of weighting the annual gridded emissions (by pollutant and source category) by the w orst and best extinction days residence times for the five

- year baseline period. To account for deposition along the trajectories, the result was further weighted by a one

- over

-

43 http://vista.cira.colostate.edu/docs/wrap/attribution/WEPMethods.doc

44 http://pah.cert. ucr.edu/aqm/308/

45 http://coha.dri.edu/index.html47 distance factor, measured as the distance in km between the centroid of each e missions grid cell and the centroid of the grid cell containing the Class I area monitoring site under investigation. (The “home” grid cell of the monitoring site was weighted by one fourth of the 36km grid cell distance, or one

- over

- 9km, to avoid a large response in that grid cell.) The resulting weighted emissions field was normalized by the highest grid cell to ease interpretation. Interpretation of the results should focus on which grid cells (or larger regions) have significant potential to affect the Class I area, and on changes between 2002 and 2018.

The WEP is not a rigorous, stand

- alone analysis, but a simple, straightforward use of existing data. As such, there are several caveats to keep in mind when using WEP results as part of a comprehens ive weight of evidence analysis:

**•** This analysis does not take into account any emissions chemistry.

**•** While actual emissions may vary considerably throughout the year, this analysis pairs up annual emissions data with 20% worst/best extinction days residenc e times

– this is likely most problematic for carbon and dust emissions, which can be highly episodic.

**•** Coarse particle and some fine particle dust emissions tend not to be transported long distances due to their large mass.

**•** The WEP results are unitless num bers, normalized to the largest

- valued grid cell. Effective use of these results requires an understanding of actual emissions values and their relative contribution to haze at a given Class I area.

Review comment: The Weighted Emissions Potential (WEP) analysis has a number of limitations, listed above. However, it may be useful as a screening tool for states to decide which source regions have the potential to contribute to haze formation at specific Class I areas.

C. Organic Aerosol Tracer

Contribu tors to organic carbon aerosols (OC) were evaluated as part of the weight of evidence for the clean conditions scenario. The CMAQ model results were analyzed to identify primary organic carbon aerosol source contributions as originating in one of three ca tegories:

**•** Primary anthropogenic OC resulting from direct organic mass emissions, such as primary organic aerosol (POA). In CMAQ these species are lumped into the term AORGPA

.

**•** Secondary anthropogenic OC resulting from aromatic VOCs, such as xylene, toluen e, and

cresols. In CMAQ these species are lumped into the term AORGA

.

**•** Secondary biogenic OC resulting from biogenic VOCs, such as terpenes. In CMAQ these species are lumped into the term AORGB

.

This analysis did not include identification of emissions s ource regions or detailed source category information. Because it was not cost effective to carry out CAMx/PSAT simulations with OC, the explicit OC results for the clean conditions case were analyzed, and then compared to the Base02b case in an attempt to infer the relative contributions of biogenic and anthropogenic VOCs to OC. These results are difficult to interpret for at least two reasons:48 **•** Because of the simplified approach used by CMAQ and the Carbon Bond Mechanism version 4 (CB4) to represent thes e species, it is not possible to accurately classify all emissions into the CMAQ model as either biogenic or anthropogenic based simply on the species name. Thus, some biogenic OC might be included with AORGA, and some anthropogenic OC might be included in AOR G B.

**•** Some fire emissions are classified as anthropogenic, but these emissions might include species such as terpenes that are typically considered biogenic. Hence, u sing this simplified approach can be misleading

.

In spite of these difficulties, howe ver, the results should classify the majority of the emissions correctly as either biogenic or anthropogenic. For each of the above three components of OC, plots of the annual average mass in the Base02b case were prepared, and then estimated the

controll able mass as the difference between the Base02b case the Base02nt clean emissions scenario. Comparing these two scenarios indicates that in the western U.S. there is considerable AORGPA mass that is not controllable. It is likely that much of this mass is from fires, since uncontrollable AORGPA mass is present at the site of large fires in southern Oregon and north of Tucson, AZ. It might be difficult for the WRAP states and tribes to use these results quantitatively in developing emissions control strateg ies for visibility SIPs and TIPs. However, the results do provide some insight into the relative contributions of biogenic and anthropogenic

OC as well as the amount of each that is controllable in the model simulations. 46

There are uncertainties in the modeled emissions of anthropogenic VOCs, and larger uncertainties in the modeled emissions of biogenic VOCs. It is not possible to evaluate the model performance individually for biogenic and anthropogenic OC because the OC measurements do not distinguish between those two forms. Instead, we can only compare total modeled OC to total measured OC. Therefore, even when the model achieves good performance for total OC, it is possible that the model may be overpredicting one component of total OC and underpredi cting the other. The inability to evaluate model performance for each component of OC increases the uncertainty of the results, so caution should be used when drawing conclusions about the sources

of OC based on these results.

Review comment: WRAP model achieves good performance for total OC, as discussed in Section 5, figure 5.1 of this document. However, as stated above, because of the potential for compensatory errors, caution should be used when drawing conclusions regarding the source of

OC based on these results.

D. Positive Matrix Factorization

As part of their Causes of Haze Analysis (CoHA) project for WRAP, Desert Research Institute (DRI) performed a Positive Matrix Factorization (PMF) analysis using the IMPROVE aerosol

data set and meteorol ogical back trajectories. PMF is one among several receptor modeling

methods. Receptor modeling is a fundamentally different approach to determining source contributions to visibility impairment in that they are based on a stati sti cal analysis of the

mon itoring data collected at the IMPROVE monitoring sites, and not on dispersion modeling.

46 Further information regarding the Organic Aerosol Tracer analysis technique can be found in the TSS document http://pah.cert.ucr.edu/aqm/308/reports/final/2006/WRAP

- RMC_2006_report_FINAL.pdf

, page 73.49 The purpose of this analysis was to distinguish chemical source profiles which could describe aerosol contributions to IMPROVE monitoring sites within the WRAP region. Through a review of source profile characteristics, profiles were identified with general or specific emissions source categories, such as “Smoke” or “Urban/Diesel” or any of several other categories. Percent contributions of each profile to each IMPR OVE site’s aerosol concentrations were derived and compared with emissions inventories to evaluate the level of confidence in the

results. 47

In order to identify the sources of aerosols in the western United States, Positive Matrix Factorization (PMF) receptor model was applied to the 24

- hr integrated aerosol chemical composition data obtained at the Class I areas of the Western Regional Air Partnership (WRAP) region through the Interagency Monitoring of Protected Visual Environments (IMPROVE) program. The IMPROVE sites in the WRAP region were grouped into 18 sub

- regions (including West Texas region). PMF is applied to each group to generate profiles of source factors. Normalized source profiles and the quantitative source contributions for each resolved factor were calculated. The major sources that contribute to the aerosol loadings in the western United States were identified. The similarities and differences of chemical source profiles, and the major aerosol source contributors in different regions of the western United States were investigated. Based on the pro file and the daily contribution to aerosol concentration of each source factor, the contributions of source factors to the aerosol light extinction coefficients were estimated using the IMPROVE method. The importance of the major aerosol sources to regiona l haze and visibility in the Class I areas of the western United States is discussed.

A trajectory analysis was used to show the major source regions of sources (factors) defined by PMF. These results were compared with the emissions inventories data to e valuate level of

confidence in results.

Review comment: The PMF and trajectory analysis may be fairly robust for the source factors identified. However, EPA believes that the grouping of IMPROVE sites into 18 sub

- regions, while less resource intensive, g ives results that are less useful than conducting PMF modeling for individual IMPROVE sites. If PMF modeling will be used in future regional haze technical analyses, then each individual IMPROVE site should be modeled. In future applications, PMF

contri bution results should be compared to other modeling methods such as CAMx using PSAT. Such a comparison could be of significant utility in evaluating upwind emission inventories used as inputs to models such as CAMx and CMAQ.

E. Causes of Dust Analysi s

As part of their CoHA project for WRAP, DRI performed a Causes of Dust Analysis, designed to characterize aerosol sampling days when coarse mass and fine soil combined constituted the dominant aerosol extinction species. In addition to categorizing dus t events, the project was also able to identify a number of temporal trends. 48

47 Further information regarding the PMF analysis can be found on the CoHA web site http://www.coha.dri.edu/web/general/tools_PMFModeling.html

. 48 Further information regarding the Causes of Dust analysis can be found on the CoHA web site http:// www.coha.dri.edu/dust/index.html

.50 The principal aim of the study was to specifically identify the primary causes of dust measured in the WRAP region by:

**•** developing a methodology for assigning worst days when d ust constituted the largest contributor to aerosol visibility extinction (worst dust days, hereafter) at IMPROVE monitors within the WRAP domain to a set of source classes; and

**•** using the methodology to categorize worst dust days over the period 2001

– 2003

.

The methodology employs several existing tools in novel ways including air mass backward trajectories, land use maps, and soil characteristics maps. In addition, two new methods have been developed as part of this work. The first is a metric for estim ating the contribution of Asian

dust to IMPROVE

- measured dust on worst dust days. The second utilizes multivariate linear regression of measured dust concentrations vs. nominally local surface meteorological data. These tools were combined using a semi

- qua ntitative approach to preliminarily determine the likely source of dust on a worst dust day at a given site. Due to limitation of the information and capabilities of the tools, the causes of some worst dust days were not determined with any

confidence. U sing 2001

- 2003 data from IMPROVE (and some protocol) monitors in the WRAP regions, each worst dust day was associated with one of these events:

**•** Transcontinental transport of large scale events from Asia

**•** Windblown dust events

**•** Transport of windblown dust fr om sources upwind (i.e. not from immediate vicinity of site).

**•** Further specification if windblown and upwind transport events appear to be regional in nature based on scale of meteorological phenomenon causing dust and number of sites

affected.

**•** Undetermine d Events

This study focused on 71 sites from the IMPROVE network (and protocol sites) located in the WRAP domain. These sites were selected based on availability of data over the 2001

– 2003 period and the availability of a nearby surface meteorological s tation over the same period.

The transport of airborne dust emitted from high wind events originating in China to the west coast of the US (about 7

– 10 days en route) has received considerable attention in recent years (Cheng et al., 2005 49 ; Park et al., 2005 50 ; Zhang et al., 2005 51 ; Darmenova et al., 2005 52

49 Cheng, TT, Lu, DR, Wang, GC, Xu, YF. Chemical characteristics of Asian dust aerosol from Hunshan Dake sandland in Northern China. Atmospheric Environment, 2005. 2903

- 2911 ). Large “Asian dust” events can contribute significantly to haze over large portions of the western US. These large Asian dust episodes are initiated by low pressure systems in the Gobi desert region

of Mongolia and northwest China. Once elevated to the troposphere, Asian dust can move fast

50 Park, SU, Chang, LS, Lee, EH. Direct radiative forcing due to aerosols in East Asia during a Hwangsa(Asian dust)

event observed on 19

- 23 March 2002 in Korea. Atmospheric Environment, 2005, 2593

- 2606

51 Zhang, RJ, Arimoto, R, An, JL, Yabuki, S, Sun, JH. Ground observations of a strong dust storm in Beij ing in March 2002. Journal of Geophysical Research, 2005, D18S06

52 Darmenova, K, Sokolik, IN, Darmenov, A. Characterization of east Asian dust outbreaks in the spring of 2001 using ground

- based and satellite data. Journal of Geophysical Research, 2005 D022 0451 under zonal flow due to the jet stream. Under high pressure ridge conditions, large

- scale exchange of dust from the troposphere to the boundary layer may occur resulti ng in elevated ground

- level mineral aerosol concentrations. Although it is difficult to quantitatively separate the influence of the Asian dust from dust that is generated on the North American continent or transported from other regions of the world (e.g. Africa), some chemical markers can help identify dust of Asian origin. Perry et al. [1997] 53 and Van Curen and Cahill [2002] 54 suggested that Al/Ca and K/Fe ratios are useful for identifying Asian and African dust. African dust is

associated with Al/Ca rati os greater than 3.8, while those ratios for Asian dust are generally less than 2.6. The K/Fe ratio is consistently above 0.5 for Asian dust, while African dust exhibits lower values for this ratio. Similar chemical markers have been adopted to help disting uish Asian dust from dust generated on the North American continent for this study. The large Asian dust storm on April 19, 1998 was used as a benchmark for establishing these markers. The dust plume from the 4/19/1998 storm crossed the Pacific Ocean, and subsided to the surface of the western

United States around 4/29/1998.

A total of 644 worst dust days, defined as 20% worst visibility days when the sum of extinction from coarse mass (CM) and (FS) was larger than any other component, were observed during the period 2001

- 2003. Using the tools described in the report, it was found that:

i. approximately 50% (318 cases) of worst dust days were attributable, with a moderate (***) to high (*****) degree of confidence, the following events/classes:

o Transcontinent al transport from Asia: 48 cases (7.5%);

o Windblown dust (generated locally in the vicinity

– nominally within 10 km

- of the site): 125 cases (19.4%);

o Upwind transport (does not involve significant windblown dust from sources local to the site): 145 cases (22.5%);

ii. Approximately 30% (190 cases) of the remaining days were attributed to the following events/classes with a low (*) to moderate (***) degree of confidence:

o Transcontinental transport from Asia: 7 cases (1.1%);

o Windblown dust: 76 cases (11.8%);

o Upw ind transport: 107 cases (16.6%);

iii. The remaining 21% of worst dust days (136 case) were not attributable to any events/classes using the tools employed in this study.

A number of temporal trends were also observed, both in terms of frequency of event occu rrence and in terms of worst dust days resulting from undetermined sources. The impact of transcontinental transport from Asia was only observed during spring (100% of 48 cases). Windblown dust as a dust causing event was most important in spring (56.8% of 125 cases), while transport from upwind sources did not vary significantly by season except for a notable decrease in the winter months (spring: 35% of 145 cases, summer: 28%, fall: 31%, winter: 6%).

53 Perry KD, Cahill TA, Eldred RA, Dutcher DD, Gill T, 1997, Long

- range transport of North African dust to the eastern United States Journal of Geophysical Research 102 (D10): 11225

- 11238

54 VanCuren RA, Cahill TA Title: Asian aerosols in North America: F requency and concentration of fine dust. Journal of Geophysical Research 107 (D24): Art. No. 4804 DEC 28 200252 For the sites considered in this study, worst dust d ays exhibited a seasonal pattern, with the most frequent occurrences in summer (246 out of 644) and spring (241). The fall (115) and winter (43) were associated with significantly fewer worst dust days. Of the 644 total worst dust days, a total

of 136 were a result of events/sources that could not be determined using the tools employed in this study. The greatest number of undetermined events occurred in the summer, corresponding to 79 cases (32% of summer worst dust days), followed by spring (23 cases, 10% of all spring worst dust days) and fall (22 cases, 19% of all fall worst dust days), and winter (12 cases, 28% of all winter worst dust days).

The results of four example case studies were examined and are presented below:

April 16, 2001 On April 16, 2001, 29 sites were classified as worst dust days. For 22 sites, the Asian Dust Score indicated a strong Asian signature. Satellite and Naval Research Laboratory model results corroborated a large Asian dust plume engulfing a large portion of the West coas t.

September 10, 2001 On September 10, 2001, 5 sites located in Arizona were classified as worst dust days. For all sites, the ADS was low (or not calculated), suggesting a negligible contribution of transported Asian dust. Trajectory analysis for all sites indicated moderate

- to high speed trajectories over

areas with moderate

- high erodibility in southeast Arizona, south New Mexico and east/southeast

Texas.

July 06, 2001 On July 06, 2001, 4 sites were classified as worst dust days. At Colombia River G orge (CORI) the LWD to Total measured dust ratio was 40.5%. However, back trajectories did not show sustained high winds over moderately (or highly) erodible terrain. Thus, CORI was assigned to a

windblown event. At Bandelier (BAND) the LWD to total measu red dust ratio was ~ 6% and trajectories showed some high winds over moderately erodible terrain. BAND was associated

with windblown event. The information available for Nearby San Pedro (SAPE) And Gila (GICL) did not provide any indication of the event t hat may have caused a worst dust day there.).

April 03, 2003 Great Sand Dunes (GRSA), Weminuche Wilderness (WEMI), and Rocky Mountain (ROMO) had worst dust days on April 03, 2003. For all three sites, the event was flagged as a regional

scale event since the same general flow pattern caused all three sites to have worst dust days.

Review comment: The Assessment of the Principal Causes of Dust

- Resultant Haze at IMPROVE Sites in the Western United States provides a reasonable basis for the attribution of transcontinental transport form Asia. The techniques employed were relatively more successful for spring events (10% of events could not be determined) than fall (19%), winter (28%) and summer events (32%)

.



### 7. BART modeling53 The Clean Air Act establishes the national goal of eliminating man

- made visibility impairment

from all Class I areas. As a part of the plan for achieving this goal Section 169A(b)(2)(A) of the act requires certain major stationary sources in existence between 1962 and 1977 to be review ed for BART. Pursuant to federal regulations, states have the option of exempting a BART

- eligible source from the BART requirements based on dispersion modeling demonstrating that the source cannot reasonably be anticipated to cause or contribute to visib ility impairment in a Class I area. According to 40 CFR Part 51, Appendix Y , a BART eligible source is considered to “contribute” to visibility impairment in a Class I area if the modeled 98th percentile change in deciviews is equal to or greater than the “contribution threshold.” Any BART

- eligible source determined to cause or contribute to visibility impairment in any Class I area is subject to BART. The EPA BART Guidelines recommend a contribution threshold of 0.5 change in deciview be used, although Sta tes have the option to establish alternative thresholds.

To determine whether a source exceeds the BART contribution threshold, EPA recommends use of the CALMET/CALPUFF modeling system; the main components of this modeling system are CALMET (a diagnostic three

- dimensional meteorological model), CALPUFF (an air quality dispersion model), and CALPOST (a post

- processing package). S even WRAP region States (Arizona, Montana, New Mexico, Nevada, South Dakota, Utah , and at the request of EPA Region 8, Montana ) re quested that the WRAP regional modeling center perform BART exemption screening modeling to help determine whether potential BART

- eligible sources in their states contribute significantly to visibility impairment at a Class I area. In response WRAP

conduct ed a modeling analysis and provided spreadsheets of Calpuff modeled 24

- hour average change in deciview from each potential BART

- eligible source and each Class I area. That CALMET/CALPUFF modeling effort is described on the RMC 55 and WRAP 56 website s. Note

th at in several of these States the WRAP modeling discussed below provided a foundation for more refined or supplemental BART

– exemption modeling conducted by the individual States. A description and evaluation of these additional modeling analyses is contain ed in the individual Regional Haze SIPs submitted by each State.

A. Overview of WRAP Modeling Approach

To conduct the modeling, WRAP followed the EPA BART guidelines (U.S. EPA, 2005) and the applicable CALMET/CALPUFF modeling guidance (e.g., IWAQM, 1998; FLAG, 2000; EPA, 2003c) in effect at the time the analysis was conducted. This included EPA’s March 16, 2006, memorandum “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF” (Atkinson and Fox, 2006). This memo was written by the EPA Off ice of Air Quality and Planning Standards (OAQPS) Model Clearinghouse Director in response to questions from EPA Region 4 on what constitutes the regulatory version of CALPUFF and recommended CALPUFF options for BART modeling in their region. The memo’s mo deling recommendations are followed in WRAP’s protocol for conducting the modeling 57

55 , except as noted below. Using the procedures in the modeling protocol, WRAP conducted initial subject

- to

- BART screening analysis modeling to determine which sources contrib ute significantly to visibility impairment at Class I areas. WRAP then provided the modeling results to the affected states, and the states used

http://pah.cert.ucr.edu/aqm/308/bart.shtml

56 http://pah.cert.ucr.edu/aqm/308/reports/final/2006/WRAP

- RMC_2006_report_FINAL.pdf

57 http://pah.cert.ucr.edu/aqm/30 8/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf54 those results, in conjunction other information, to determine whether or not the individual

BART

- eligible sourc e will be subject to BART control requirements. For some parameters WRAP modeled multiple scenarios and deferred to the States to select which modeled parameter is most appropriate. For example WRAP modeled background visibility conditions corresponding to both the 20% best visibility days and annual average visibility days. EPA guidance allows States to select either characterization of background conditions for their BART modeling. WRAP also provided modeling results showing the highest 24 average change in visibility and the 8th highest (98th percentile) predicted change. States may utilize the maximum result if a conservative screening analysis is deemed appropriate. The Regional Haze SIPs submitted by each State should be reviewed to determine which of the WRAP BART modeling options was selected for use by the State.

B. Model Selection and Applicability

Relevant guidance (IWAQM, 1998 58 ; FLAG 2000 59 ; EPA, 2003 60 ) states that the CALPUFF model is generally applicable at distances from 50 km to at least 200

- 30 0 km downwind of a source. Given the large number of Class I areas in the West, most BART

- eligible stationary sources modeled by WRAP were located within 300 km of at least one Class I area. In some cases where the transport distance to the nearest Class I area exceeded 300 km the States, in consultation with EPA and the FLM’s, have conducted supplemental analysis for their regional

haze BART submittals.

The WRAP modeling was conducted using CALPUFF version 6.0. At the time of the analysis the Calpuff regu latory version was v 5.711, however discussions with the Federal Land Managers (FLMs) and others revealed that this version of the model contained software bugs and that a newer version should be used. EPA’s regulatory version has subsequently been revised to version 5.8 which should be used in new regulatory applications. Calpuff version 6.0 (and version 5.711) is considered obsolete; however, it was considered appropriate at the time of the analysis and is therefore acceptable for use in State regional ha ze plans that have relied on WRAPs modeling.

C. Model ing Domain and Meteorology I nputs

The WRAP RMC developed a set of CALMET/CALPUFF modeling domains in the contiguous U.S. that focus on the following states and nearby Class I areas: Arizona, Montana, Ne w Mexico, Nevada, South Dakota, and Utah. In addition, there is an Alaska CALMET/CALPUFF modeling domain. The dimensions of the domain were selected to include the State of interest, plus Class I areas in nearby states, and to provide a sufficient buffer between the Class I areas and the domain boundaries (e.g., 50 km) to assure that CALPUFF puffs are not eliminated that may temporarily leave the domain and later reemerge and cause visibility impacts at a Class I area. WRAP

58 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts (EPA

- 454/R

- 98

- 019)

59 http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf

60 EPA. 2003 . “Revisions to the Guideline on Air Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations.

40 CFR51.55 applied CALPUFF/CALMET for each of these domains. For the five continental

- U.S. domains,

WRAP used a 4

- km grid and 11 vertical layers with a Lambert conformal conic (LCC) map projection identical to that used for the WRAP MM5 and CMAQ modeling (see Section 1 .3 of the WRAP 2002 MPE repor t 61 . The CALMET modeling for these five states used MM5 meteorological data at 36

- km resolution and terrain and land use data at 4

- km resolution, along with surface meteorological and precipitation observations. For the WRAP Alaska CALMET/CALPUFF modeling, a 2

- km grid was used, along with 15

- km

- resolution MM5 data, surface meteorological and precipitation data, and 1

- km

- resolution land use and terrain data. For CALMET modeling for the continental

- U.S. states, WRAP used MM5 meteorology data for the years 2001 , 2002 and 2003. EPA recommends performing three years of CALMET/CALPUFF modeling when using MM5 data, and 2001 through 2003 were the most recent three years with MM5 data available at the time this modeling was initiated. For Alaska CALMET modeling,

WRAP used one year of data (2002) because only one year of historical MM5 modeling is

available.

Note that although surface meteorological and precipitation observations were used as input to CALMET, upper

- air meteorological observations were not. EPA and the FLMs generally recommend both surface and upper air information be blended into the CALMET modeling, although in some applications they may be redundant to the MM5 data which already includes the upper air data.

D. Emissions I nput

According to the EPA BAR T Guidelines: “The emissions estimates used in the models are intended to reflect steady

- state operating conditions during periods of high capacity utilization. We do not generally recommend that emissions reflecting periods of start

- up, shutdown, and malf unction be used, as such emission rates could produce higher than normal effects than would be typical of most facilities. We recommend that States use the 24 hour average actual emission rate from the highest emitting day of the meteorological period mode led, unless this rate reflects periods start

- up, shutdown, or malfunction.” (EPA, 2005) 62

.

As a first approach to identify BART eligible sources and the appr opriate modeling emission

rates, WRAP used SCC

- based BART eligibility criteria and information prov ided by ERG, Inc., in their report “Identification of BART

- eligible Sources in the WRAP Region” (ERG, 2006 ) 63

61 . WRAP then provided spreadsheets with this information to the states and tribes for comment. The States/Tribes reviewed this information along with NSR permitting and other records and determined which sources are BART eligible. Sources from the 7 states determined to be BART eligible were then modeled using emission estimates provided by the state air agency and in the

case of Montana BART

- eligible sources, by EPA Region 8 based on the maximum 24 hour emission rates during the 2001

- 2003 meteorological period modeled. In

- stack continuous

http://pah.cert.ucr.edu/aqm/308/docs.shtml

62 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) De terminations”. Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104

- 39172. 40 CFR Part 51, FRL

7925

- 9, RIN AJ31.

63 ERG, Inc., 2006: Identification of BART

- eligible Sources in the WRAP Region, Draft Report. Prepared for the

Western Regional Air Partnership by ERG, Inc., Sacramento, CA. April 4, 200656 emission monitoring (CEM) data were used where available. For sources where CEM data were not available permitted a llowable or calculated AP

- 42 emission rates were used. For source specific emissions information the individual State regional haze SIP should be consulted.

E. Calpuff/Calmet Model S ettings

The CALMET and CALPUFF model settings used by WRAP complied with E PA’s recommendations for regulatory application of the CALPUFF modeling system in effect at the time of the analysis, except as noted below:

i. The maximum allowable mixing height (Z1MAX) for the WRAP CALMET modeling was 4,500 m AGL, versus the EPA regulato ry default of 3,000 m AGL. Vertical temperature soundings indicate higher summertime mixing heights are appropriate in many areas of the intermountain west.

ii. The EPA default assumes no MM5 data will be used (IPROG=0). In the RMC BART screening analysis, MM 5 data were used as an initial

- guess field (IPROG=14).

iii. The EPA default assumes values for IEXTRP (

- 4) and RMIN2 (4) that are incompatible with each other in some of the applications and therefore used values of 1 and 4, respectively. WRAP used the hourly 36

- km MM5 data to define the upper

- level winds, thus the extrapolation of the surface wind data aloft as recommended by EPA (IEXTRP=

- 4) was not needed.

iv. WRAP set IAVET to 0 to turn off spatial averaging of the temperature interpolation, since the MM5 temp eratures are already fairly smooth.

v. The beginning and ending water land use categories were changed to 51 and 55, respectively, rather than using the EPA default (999) that assumes no water land use categories.

F. Model E valuation

The CALMET meteorologica l fields were spot

- checked for reasonableness using visualization and animation tools, but no comprehensive evaluation was undertaken. The main inputs to CALMET are the hourly 36

- km MM5 data (15

- km MM5 data for Alaska), surface meteorological and precipita tion measurements, terrain data, and land use data. The 36

- km MM5 fields were evaluated previously (McNally, 2003 64 ; Kemball

- Cook et al., 2005 65 ; Baker,

2004a 66 ,b 67

64 McNally, D.E. 2003. Annual Application of MM5 for Calendar Year 2001. Prepared for U.S. EPA, Office of Air Quality and Planning Standards. Prepared by Alpine Geophysics, Arvada, CO. ), and the terrain and land use inputs were evaluated by comparing spatial plots of the inputs a gainst topographic and land use maps. The Alaska 15

- km MM5 data were also evaluated (Kemball

- Cook et al., 2005). EPA has reviewed the WRAP CALMET fields for the 6 modeling domains and found them adequate for use by States initially in BART Calpuff modeling applications. Several States have modified the WRAP CALMET analysis to account for site

65 Kemball

- Cook, S., Y. Jia, C. Emery and R. Morris. 2005. Alaska MM5 Modeling for the 2002 Annual Period to Support Visibility Modeling. Western Regional Air Partnership, Regional Modeling Center.

66 Baker, K. 2004a. Summer MM5 Performance. Midwest Regional Planning Organization.

67 Baker, K. 2004b. Monthly Rainfall Evaluation. Midwest regional Planning Organization.57 specific issues such as nearby terrain features in their BART modeling. The individual State regional haze plans provide details on these modifications.

Calpuff has been evaluated nationally by EPA and is considered appropriate for long range transport applications and no site specific performance evaluations were performed.

Review Comment (summary of EPA comments on WRAP BART Modeling): WRAP conducted initial subje ct

- to

- BART screening analysis modeling for s even western States to determine which sources contribute significantly to visibility impairment in Class I areas. The overall approach followed EPA guidance and modeling procedures in effect at the time of the a nalysis and is therefore acceptable for use in the current regional haze plans. In several of these States the WRAP modeling provided a foundation for more refined or supplemental BART

– exemption modeling conducted by the individual States. A description an d evaluation of these additional modeling analyses is contained in the individual Regional Haze SIPs submitted by each State.



### 8. Conclusions

– WRAP Technical Work In Support of Regional Haze Planning

The technical work of the WRAP was consistent with the b est practices and science at the time it was performed. It is appropriate for the western states to use it as the basis for complying with the requirements of the Regional Haze Rule.

The EPA’s evaluation of the WRAPs work is summarized below, organized by topic area.

A. Baseline Visibility Conditions

The WRAP followed EPA guidance and used IMPROVE program

- calculated data in developing the baseline visibility conditions for the Class I areas in their region. They appropriately

extended EPA

- recommended met hods in some cases in order to ensure a sufficiently large data set for determining baseline conditions.

B. Natural Visibility Conditions

WRAP used a refined method to estimate natural visibility conditions. The states are able to do

this under the rule. WRAP’s method matches EPA’s general approach, but is more refined and statistically sophisticated than the default EPA procedure.

C. Source Contribution to Haze

i. PM Source Apportionment Technology Analysis : The PSAT analysis provides useful qualitative information about source contributions to visibility impairment. It is appropriate for states to consider the results of these analyses in their reasonable progress

demonstrations.

ii. Weighted Emissions Potential Analysis : The Weighted Emissions Potential a nalysis is a simple integration of emissions and wind data. It is potentially useful as a screening tool,58 but has a number of limitations. It should be used only in conjunction with other

methods.

iii. Organic Aerosol Tracer Analysis : The organic aerosol trac er analysis made a good case for the fact that there is a significant amount of uncontrollable, organic aerosols contributing to haze in the West. However, further analysis would be required in order to use this knowledge to change assumptions about natura l conditions or to make the case that it is unreasonable to further control anthropogenic organic aerosols.

iv. Positive Matrix Factorization analysis : The positive matrix factorization analysis is useful for identifying source categories impacting class one areas as long as those source categories were evaluated in the analysis.

v. Causes of Dust Analysis : The Causes of Dust Analysis provides a reasonable account of transcontinental transport of dust. The techniques were more successful for spring events

than other times of the year. If a particular Class I area is significantly impacted by coarse mass, this analysis could be useful it characterizing the extent to which those coarse mass impacts are controllable by the state.

D. Reasonable Progress Goals.

Th e reasonable progress goals are based on the WRAP’s projection of visibility impairment at Class I areas in 2018. This projection is derived from the results of the visibility modeling. The visibility modeling depends on the meteorological modeling and the emissions inventory.

Emissions Inventory : The emissions inventory was completed in accordance with EPA guidance and the final versions of the 2002 and 2018 inventories were complete, recent and accurate. The inventories should be adequate for all of the Class I areas covered by the WRAP analysis

. i. Meteorological Modeling : The meteorological model and methodology used by WRAP

was state

- of

- the

- science at the time the modeling was conducted. The performance of the model was adequate for use in developing t he reasonable progress goals. For future modeling, additional attention should be paid to performance in the desert southwest. The performance of the model could be further improved for this area.

ii. Visibility Modeling : The visibility model and methodology used by WRAP was state

- of

the

- science at the time the modeling was conducted. The performance of the model was adequate for use in developing the reasonable progress goals. For future modeling, the performance should be analyzed at the geographic sub

- regi on level. Evaluating performance over such a large area can mask performance problems at particular Class 1 areas. Any sub

- region performance analysis should examine the effects of any remaining performance concerns with the meteorological modeling. Given the broad geographic scope in the emissions inventories and of the model performance analysis, a more detailed analysis of the model performance and relevant emissions inventory source categories may be required for some Class I areas.59 

### A. Appendix: Accessing WRAP Technical P roducts

The following text in italics is the executive summary of a document created by WRAP titled “TSS Roadmap and Users Guide”. The WRAP Technical Support System (TSS) is publicly accessible over the internet and is the primary method WRAP uses to convey technical products to State and Tribes in support of their RH SIP development efforts.

The Western Regional Air Partnership ‘s (WRAP) 68 Technical Support System (TSS) 69 is intended to present and disseminate technical regional haze plan ning results for the 116 Class I areas in the WRAP region on an ongoing basis, identify associated technical data and analysis products and resources, and describe how these data and results were derived and relate to one another. The TSS presents air qua lity indicators for regional haze using a “weight

- of

- evidence” approach as suggested in EPA guidance

– monitoring, emissions, source apportionment, and visibility modeling results. The TSS data displays and analysis results are formatted to comply with th e metrics and specific requirements in the EPA Regional Haze Rule (RHR) 70 and are being used by states and EPA regional offices in the WRAP region, in the technical support documents for State or Federal Implementation Plans (SIPs and FIPs) required for eac h Class I areas under the RHR. The methodologies for technical data collection and processing, quality assurance and control, and analysis activities are documented on the TSS.

Much of the data and regional analysis results on the TSS are also suitable , and have been used for other air quality analysis and planning purposes, because they were developed at the direction of, and through the collaborative committee and workgroup efforts within the WRAP organization . A significant amount of more detailed d ata and/or analysis results also exist in data support systems ( VIEWS 71

. EDMS 72

, FETS 73 ) or separate projects ( RMC 74

, CoHA 75 ), all of which support the TSS summary data developed to support RHR air quality planning. These systems or projects are listed, descri bed, and linked to on the Projects section of the TSS. Also, many related reports for specific source sectors, analysis of regional impacts to Class I areas, and control strategy analyses are found under individual Committees, Forums, and Workgroups

on th e WRAP website

– access to these results are generally not found on the TSS due to their geographic

- and/or source

- specific nature, and may be applied by the appropriate regulatory jurisdiction for RHR planning as that agency wishes. This version of the W RAP website moved into archive status in early 2010, but the links will remain accessible.

On the TSS under the “Home” section, links to administrative and reference information are provided to assure feedback to keep the TSS current. Under the “Resource s” section, descriptions, documentation, and access to tools and data are provided, for the Haze Planning

, Monitoring

, Emissions

, Modeling , and Apportionment results required in a State or Federal

68 http://www.wrapair2.org/ to access http://www.wrapair.org

69 http://vista.cira.colostate.edu/tss/

70 http://www.wrapair.org/RH_Rule_P51/index.html

71 http://vie ws.cira.colostate.edu/web/

72 http://www.wrapedms.org/

73 http://www.wrapfets.org/

74 http://pah.cert.ucr.edu/aqm/308/

75 http://www.coha.dri.edu/60 Implementation Plan under the Regional Haze Rule, as well a s the subsequent implementation of those plans.

Required Technical Elements on TSS for Regional Haze Implementation Plans

Monitored baseline visibility conditions (2000

- 04) for each Class I area, plus subsequent data
Natural conditions estimates for ea ch Class I area (2064 target)

Uniform glide slope for each Class I area

Baseline emissions (2000

- 2004)

Projected 2018 emissions

Source apportionment at each Class I area for 2000

- 04 baseline and 2018 projections

Projected 2018 visibility conditions at each Class I area

The “Projects” section of the TSS provides brief descriptions of the data systems and projects, and links to the more detailed and voluminous results found in each system or project. Finally, the “Partners” section provides links to t he team behind the TSS, including descriptions of their expertise and experience.

All data visualization and analysis tools on the TSS have integrated access to “Help” for the user, via the master TSS “ Getting Started ” document, an HTML page. The Monitor ing, Emissions, Modeling, and Source Apportionment subsections contain detailed “Methods” descriptions, identifying how datasets and analysis tools were prepared and how they are used

on the TSS.